id
stringlengths 9
10
| submitter
stringlengths 5
47
⌀ | authors
stringlengths 5
1.72k
| title
stringlengths 11
234
| comments
stringlengths 1
491
⌀ | journal-ref
stringlengths 4
396
⌀ | doi
stringlengths 13
97
⌀ | report-no
stringlengths 4
138
⌀ | categories
stringclasses 1
value | license
stringclasses 9
values | abstract
stringlengths 29
3.66k
| versions
listlengths 1
21
| update_date
int64 1,180B
1,718B
| authors_parsed
sequencelengths 1
98
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1201.2004 | Md. Amjad Hossain | Md. Amjad Hossain, Pintu Chandra Shill, Bishnu Sarker, and Kazuyuki
Murase | Optimal Fuzzy Model Construction with Statistical Information using
Genetic Algorithm | null | null | 10.5121/ijcsit.2011.3619 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Fuzzy rule based models have a capability to approximate any continuous
function to any degree of accuracy on a compact domain. The majority of FLC
design process relies on heuristic knowledge of experience operators. In order
to make the design process automatic we present a genetic approach to learn
fuzzy rules as well as membership function parameters. Moreover, several
statistical information criteria such as the Akaike information criterion
(AIC), the Bhansali-Downham information criterion (BDIC), and the
Schwarz-Rissanen information criterion (SRIC) are used to construct optimal
fuzzy models by reducing fuzzy rules. A genetic scheme is used to design
Takagi-Sugeno-Kang (TSK) model for identification of the antecedent rule
parameters and the identification of the consequent parameters. Computer
simulations are presented confirming the performance of the constructed fuzzy
logic controller.
| [
{
"version": "v1",
"created": "Tue, 10 Jan 2012 10:14:33 GMT"
}
] | 1,326,240,000,000 | [
[
"Hossain",
"Md. Amjad",
""
],
[
"Shill",
"Pintu Chandra",
""
],
[
"Sarker",
"Bishnu",
""
],
[
"Murase",
"Kazuyuki",
""
]
] |
1201.2711 | Fionn Murtagh | Fionn Murtagh | Ultrametric Model of Mind, I: Review | 20 pages, 2 figures, 46 references. arXiv admin note: substantial
text overlap with arXiv:0709.0116, arXiv:0805.2744, and arXiv:1105.0121 (V3:
2 typos corrected) | p-Adic Numbers, Ultrametric Analysis and Applications, 4, 193-206,
2012 | 10.1134/S2070046612030041 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We mathematically model Ignacio Matte Blanco's principles of symmetric and
asymmetric being through use of an ultrametric topology. We use for this the
highly regarded 1975 book of this Chilean psychiatrist and pyschoanalyst (born
1908, died 1995). Such an ultrametric model corresponds to hierarchical
clustering in the empirical data, e.g. text. We show how an ultrametric
topology can be used as a mathematical model for the structure of the logic
that reflects or expresses Matte Blanco's symmetric being, and hence of the
reasoning and thought processes involved in conscious reasoning or in reasoning
that is lacking, perhaps entirely, in consciousness or awareness of itself. In
a companion paper we study how symmetric (in the sense of Matte Blanco's)
reasoning can be demarcated in a context of symmetric and asymmetric reasoning
provided by narrative text.
| [
{
"version": "v1",
"created": "Fri, 13 Jan 2012 00:17:17 GMT"
},
{
"version": "v2",
"created": "Mon, 6 Feb 2012 19:47:26 GMT"
},
{
"version": "v3",
"created": "Mon, 16 Jul 2012 12:43:58 GMT"
}
] | 1,474,329,600,000 | [
[
"Murtagh",
"Fionn",
""
]
] |
1201.3107 | Li Yang | Li Yang, Yuhui Wang | Tacit knowledge mining algorithm based on linguistic truth-valued
concept lattice | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/3.0/ | This paper is the continuation of our research work about linguistic
truth-valued concept lattice. In order to provide a mathematical tool for
mining tacit knowledge, we establish a concrete model of 6-ary linguistic
truth-valued concept lattice and introduce a mining algorithm through the
structure consistency. Specifically, we utilize the attributes to depict
knowledge, propose the 6-ary linguistic truth-valued attribute extended context
and congener context to characterize tacit knowledge, and research the
necessary and sufficient conditions of forming tacit knowledge. We respectively
give the algorithms of generating the linguistic truth-valued congener context
and constructing the linguistic truth-valued concept lattice.
| [
{
"version": "v1",
"created": "Sun, 15 Jan 2012 17:33:28 GMT"
}
] | 1,326,758,400,000 | [
[
"Yang",
"Li",
""
],
[
"Wang",
"Yuhui",
""
]
] |
1201.3204 | Alex Fukunaga | Akihiro Kishimoto, Alex Fukunaga, Adi Botea | Evaluation of a Simple, Scalable, Parallel Best-First Search Strategy | in press, to appear in Artificial Intelligence | Artificial Intelligence (2013), vol. 195, pp. 222-248 | 10.1016/j.artint.2012.10.007 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large-scale, parallel clusters composed of commodity processors are
increasingly available, enabling the use of vast processing capabilities and
distributed RAM to solve hard search problems. We investigate Hash-Distributed
A* (HDA*), a simple approach to parallel best-first search that asynchronously
distributes and schedules work among processors based on a hash function of the
search state. We use this approach to parallelize the A* algorithm in an
optimal sequential version of the Fast Downward planner, as well as a 24-puzzle
solver. The scaling behavior of HDA* is evaluated experimentally on a shared
memory, multicore machine with 8 cores, a cluster of commodity machines using
up to 64 cores, and large-scale high-performance clusters, using up to 2400
processors. We show that this approach scales well, allowing the effective
utilization of large amounts of distributed memory to optimally solve problems
which require terabytes of RAM. We also compare HDA* to Transposition-table
Driven Scheduling (TDS), a hash-based parallelization of IDA*, and show that,
in planning, HDA* significantly outperforms TDS. A simple hybrid which combines
HDA* and TDS to exploit strengths of both algorithms is proposed and evaluated.
| [
{
"version": "v1",
"created": "Mon, 16 Jan 2012 10:31:47 GMT"
},
{
"version": "v2",
"created": "Thu, 25 Oct 2012 03:39:16 GMT"
}
] | 1,426,809,600,000 | [
[
"Kishimoto",
"Akihiro",
""
],
[
"Fukunaga",
"Alex",
""
],
[
"Botea",
"Adi",
""
]
] |
1201.3408 | Velimir Ilic | Milos B. Djuric, Velimir M. Ilic and Miomir S. Stankovic | The computation of first order moments on junction trees | 9 pages, 1 figure | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We review some existing methods for the computation of first order moments on
junction trees using Shafer-Shenoy algorithm. First, we consider the problem of
first order moments computation as vertices problem in junction trees. In this
way, the problem is solved using the memory space of an order of the junction
tree edge-set cardinality. After that, we consider two algorithms,
Lauritzen-Nilsson algorithm, and Mau\'a et al. algorithm, which computes the
first order moments as the normalization problem in junction tree, using the
memory space of an order of the junction tree leaf-set cardinality.
| [
{
"version": "v1",
"created": "Tue, 17 Jan 2012 01:28:55 GMT"
}
] | 1,326,844,800,000 | [
[
"Djuric",
"Milos B.",
""
],
[
"Ilic",
"Velimir M.",
""
],
[
"Stankovic",
"Miomir S.",
""
]
] |
1201.4080 | Ingmar Steiner | Ingmar Steiner (INRIA Lorraine - LORIA), Slim Ouni (INRIA Lorraine -
LORIA) | Progress in animation of an EMA-controlled tongue model for
acoustic-visual speech synthesis | null | Elektronische Sprachsignalverarbeitung 2011 TUDpress (Ed.) (2011)
245-252 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a technique for the animation of a 3D kinematic tongue model, one
component of the talking head of an acoustic-visual (AV) speech synthesizer.
The skeletal animation approach is adapted to make use of a deformable rig
controlled by tongue motion capture data obtained with electromagnetic
articulography (EMA), while the tongue surface is extracted from volumetric
magnetic resonance imaging (MRI) data. Initial results are shown and future
work outlined.
| [
{
"version": "v1",
"created": "Thu, 19 Jan 2012 15:29:56 GMT"
}
] | 1,327,017,600,000 | [
[
"Steiner",
"Ingmar",
"",
"INRIA Lorraine - LORIA"
],
[
"Ouni",
"Slim",
"",
"INRIA Lorraine -\n LORIA"
]
] |
1201.5426 | M. H. van Emden | A. Nait Abdallah and M.H. van Emden | Constraint Propagation as Information Maximization | 21 pages | null | null | Research Report 746, Dept. of Computer Science, University of
Western Ontario, Canada | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper draws on diverse areas of computer science to develop a unified
view of computation:
(1) Optimization in operations research, where a numerical objective function
is maximized under constraints, is generalized from the numerical total order
to a non-numerical partial order that can be interpreted in terms of
information. (2) Relations are generalized so that there are relations of which
the constituent tuples have numerical indexes, whereas in other relations these
indexes are variables. The distinction is essential in our definition of
constraint satisfaction problems. (3) Constraint satisfaction problems are
formulated in terms of semantics of conjunctions of atomic formulas of
predicate logic. (4) Approximation structures, which are available for several
important domains, are applied to solutions of constraint satisfaction
problems.
As application we treat constraint satisfaction problems over reals. These
cover a large part of numerical analysis, most significantly nonlinear
equations and inequalities. The chaotic algorithm analyzed in the paper
combines the efficiency of floating-point computation with the correctness
guarantees of arising from our logico-mathematical model of
constraint-satisfaction problems.
| [
{
"version": "v1",
"created": "Thu, 26 Jan 2012 01:42:18 GMT"
},
{
"version": "v2",
"created": "Thu, 7 Feb 2013 23:05:29 GMT"
}
] | 1,360,540,800,000 | [
[
"Abdallah",
"A. Nait",
""
],
[
"van Emden",
"M. H.",
""
]
] |
1201.5472 | Pierrick Tranouez | Pierrick Tranouez (LITIS), Eric Daud\'e (IDEES), Patrice Langlois
(IDEES) | A multiagent urban traffic simulation | arXiv admin note: significant text overlap with arXiv:0909.1021 and
arXiv:0910.1026 | Journal of Nonlinear Systems and Applications 1, 3 (2010) 9 pp (in
print) | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We built a multiagent simulation of urban traffic to model both ordinary
traffic and emergency or crisis mode traffic. This simulation first builds a
modeled road network based on detailed geographical information. On this
network, the simulation creates two populations of agents: the Transporters and
the Mobiles. Transporters embody the roads themselves; they are utilitarian and
meant to handle the low level realism of the simulation. Mobile agents embody
the vehicles that circulate on the network. They have one or several
destinations they try to reach using initially their beliefs of the structure
of the network (length of the edges, speed limits, number of lanes etc.).
Nonetheless, when confronted to a dynamic, emergent prone environment (other
vehicles, unexpectedly closed ways or lanes, traffic jams etc.), the rather
reactive agent will activate more cognitive modules to adapt its beliefs,
desires and intentions. It may change its destination(s), change the tactics
used to reach the destination (favoring less used roads, following other
agents, using general headings), etc. We describe our current validation of our
model and the next planned improvements, both in validation and in
functionalities.
| [
{
"version": "v1",
"created": "Thu, 26 Jan 2012 10:15:09 GMT"
}
] | 1,327,622,400,000 | [
[
"Tranouez",
"Pierrick",
"",
"LITIS"
],
[
"Daudé",
"Eric",
"",
"IDEES"
],
[
"Langlois",
"Patrice",
"",
"IDEES"
]
] |
1201.5841 | Alexandre Castro | Alexandre de Castro | The thermodynamic cost of fast thought | null | null | 10.1007/s11023-013-9302-x | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | After more than sixty years, Shannon's research [1-3] continues to raise
fundamental questions, such as the one formulated by Luce [4,5], which is still
unanswered: "Why is information theory not very applicable to psychological
problems, despite apparent similarities of concepts?" On this topic, Pinker
[6], one of the foremost defenders of the computational theory of mind [6], has
argued that thought is simply a type of computation, and that the gap between
human cognition and computational models may be illusory. In this context, in
his latest book, titled Thinking Fast and Slow [8], Kahneman [7,8] provides
further theoretical interpretation by differentiating the two assumed systems
of the cognitive functioning of the human mind. He calls them intuition (system
1) determined to be an associative (automatic, fast and perceptual) machine,
and reasoning (system 2) required to be voluntary and to operate logical-
deductively. In this paper, we propose an ansatz inspired by Ausubel's learning
theory for investigating, from the constructivist perspective [9-12],
information processing in the working memory of cognizers. Specifically, a
thought experiment is performed utilizing the mind of a dual-natured creature
known as Maxwell's demon: a tiny "man-machine" solely equipped with the
characteristics of system 1, which prevents it from reasoning. The calculation
presented here shows that [...]. This result indicates that when the system 2
is shut down, both an intelligent being, as well as a binary machine, incur the
same energy cost per unit of information processed, which mathematically proves
the computational attribute of the system 1, as Kahneman [7,8] theorized. This
finding links information theory to human psychological features and opens a
new path toward the conception of a multi-bit reasoning machine.
| [
{
"version": "v1",
"created": "Fri, 27 Jan 2012 17:25:29 GMT"
},
{
"version": "v2",
"created": "Fri, 10 Feb 2012 03:11:06 GMT"
},
{
"version": "v3",
"created": "Mon, 12 Nov 2012 12:44:49 GMT"
},
{
"version": "v4",
"created": "Sat, 26 Jan 2013 14:37:56 GMT"
}
] | 1,471,132,800,000 | [
[
"de Castro",
"Alexandre",
""
]
] |
1201.6511 | Gilles Falquet | Claudine M\'etral, Gilles Falquet, Kostas Karatzas | Ontologies for the Integration of Air Quality Models and 3D City Models | null | In Conceptual Models for Practitioners, J. Teller, C. Tweed, G.
Rabino (Eds.), Societ\`a Editrice Esculapio, Bologna, 2008 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The holistic approach to sustainable urban planning implies using different
models in an integrated way that is capable of simulating the urban system. As
the interconnection of such models is not a trivial task, one of the key
elements that may be applied is the description of the urban geometric
properties in an "interoperable" way. Focusing on air quality as one of the
most pronounced urban problems, the geometric aspects of a city may be
described by objects such as those defined in CityGML, so that an appropriate
air quality model can be applied for estimating the quality of the urban air on
the basis of atmospheric flow and chemistry equations.
In this paper we first present theoretical background and motivations for the
interconnection of 3D city models and other models related to sustainable
development and urban planning. Then we present a practical experiment based on
the interconnection of CityGML with an air quality model. Our approach is based
on the creation of an ontology of air quality models and on the extension of an
ontology of urban planning process (OUPP) that acts as an ontology mediator.
| [
{
"version": "v1",
"created": "Tue, 31 Jan 2012 11:31:50 GMT"
}
] | 1,328,054,400,000 | [
[
"Métral",
"Claudine",
""
],
[
"Falquet",
"Gilles",
""
],
[
"Karatzas",
"Kostas",
""
]
] |
1202.0440 | Matej Hoffmann | Matej Hoffmann and Rolf Pfeifer | The implications of embodiment for behavior and cognition: animal and
robotic case studies | Book chapter in W. Tschacher & C. Bergomi, ed., 'The Implications of
Embodiment: Cognition and Communication', Exeter: Imprint Academic, pp. 31-58 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we will argue that if we want to understand the function of
the brain (or the control in the case of robots), we must understand how the
brain is embedded into the physical system, and how the organism interacts with
the real world. While embodiment has often been used in its trivial meaning,
i.e. 'intelligence requires a body', the concept has deeper and more important
implications, concerned with the relation between physical and information
(neural, control) processes. A number of case studies are presented to
illustrate the concept. These involve animals and robots and are concentrated
around locomotion, grasping, and visual perception. A theoretical scheme that
can be used to embed the diverse case studies will be presented. Finally, we
will establish a link between the low-level sensory-motor processes and
cognition. We will present an embodied view on categorization, and propose the
concepts of 'body schema' and 'forward models' as a natural extension of the
embodied approach toward first representations.
| [
{
"version": "v1",
"created": "Thu, 2 Feb 2012 14:25:38 GMT"
}
] | 1,328,227,200,000 | [
[
"Hoffmann",
"Matej",
""
],
[
"Pfeifer",
"Rolf",
""
]
] |
1202.0837 | Jose Hernandez-Orallo | Javier Insa-Cabrera, Jose-Luis Benacloch-Ayuso, Jose Hernandez-Orallo | On the influence of intelligence in (social) intelligence testing
environments | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper analyses the influence of including agents of different degrees of
intelligence in a multiagent system. The goal is to better understand how we
can develop intelligence tests that can evaluate social intelligence. We
analyse several reinforcement algorithms in several contexts of cooperation and
competition. Our experimental setting is inspired by the recently developed
Darwin-Wallace distribution.
| [
{
"version": "v1",
"created": "Fri, 3 Feb 2012 22:38:04 GMT"
}
] | 1,426,809,600,000 | [
[
"Insa-Cabrera",
"Javier",
""
],
[
"Benacloch-Ayuso",
"Jose-Luis",
""
],
[
"Hernandez-Orallo",
"Jose",
""
]
] |
1202.1886 | Sodbileg Shirmen | N.Ugtakhbayar, D.Battulga and Sh.Sodbileg | Classification of artificial intelligence ids for smurf attack | 6 pages, 5 figures, 1 table | IJAIA (2012); | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many methods have been developed to secure the network infrastructure and
communication over the Internet. Intrusion detection is a relatively new
addition to such techniques. Intrusion detection systems (IDS) are used to find
out if someone has intrusion into or is trying to get it the network. One big
problem is amount of Intrusion which is increasing day by day. We need to know
about network attack information using IDS, then analysing the effect. Due to
the nature of IDSs which are solely signature based, every new intrusion cannot
be detected; so it is important to introduce artificial intelligence (AI)
methods / techniques in IDS. Introduction of AI necessitates the importance of
normalization in intrusions. This work is focused on classification of AI based
IDS techniques which will help better design intrusion detection systems in the
future. We have also proposed a support vector machine for IDS to detect Smurf
attack with much reliable accuracy.
| [
{
"version": "v1",
"created": "Thu, 9 Feb 2012 04:28:16 GMT"
}
] | 1,328,832,000,000 | [
[
"Ugtakhbayar",
"N.",
""
],
[
"Battulga",
"D.",
""
],
[
"Sodbileg",
"Sh.",
""
]
] |
1202.1891 | Ei Shwe Sin | Ei Shwe Sin, Nang Saing Moon Kham | Hyper heuristic based on great deluge and its variants for exam
timetabling problem | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/3.0/ | Today, University Timetabling problems are occurred annually and they are
often hard and time consuming to solve. This paper describes Hyper Heuristics
(HH) method based on Great Deluge (GD) and its variants for solving large,
highly constrained timetabling problems from different domains. Generally, in
hyper heuristic framework, there are two main stages: heuristic selection and
move acceptance. This paper emphasizes on the latter stage to develop Hyper
Heuristic (HH) framework. The main contribution of this paper is that Great
Deluge (GD) and its variants: Flex Deluge(FD), Non-linear(NLGD), Extended Great
Deluge(EGD) are used as move acceptance method in HH by combining Reinforcement
learning (RL).These HH methods are tested on exam benchmark timetabling problem
and best results and comparison analysis are reported.
| [
{
"version": "v1",
"created": "Thu, 9 Feb 2012 05:51:18 GMT"
}
] | 1,328,832,000,000 | [
[
"Sin",
"Ei Shwe",
""
],
[
"Kham",
"Nang Saing Moon",
""
]
] |
1202.1945 | Jayabrabu R | R. Jayabrabu, V. Saravanan, K. Vivekanandan | A framework: Cluster detection and multidimensional visualization of
automated data mining using intelligent agents | 15 pages | International Journal of Artificial Intelligence & Applications
(IJAIA), Vol.3, No.1, January 2012 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Data Mining techniques plays a vital role like extraction of required
knowledge, finding unsuspected information to make strategic decision in a
novel way which in term understandable by domain experts. A generalized frame
work is proposed by considering non - domain experts during mining process for
better understanding, making better decision and better finding new patters in
case of selecting suitable data mining techniques based on the user profile by
means of intelligent agents. KEYWORDS: Data Mining Techniques, Intelligent
Agents, User Profile, Multidimensional Visualization, Knowledge Discovery.
| [
{
"version": "v1",
"created": "Thu, 9 Feb 2012 10:57:53 GMT"
}
] | 1,328,832,000,000 | [
[
"Jayabrabu",
"R.",
""
],
[
"Saravanan",
"V.",
""
],
[
"Vivekanandan",
"K.",
""
]
] |
1202.3698 | Udi Apsel | Udi Apsel, Ronen I. Brafman | Extended Lifted Inference with Joint Formulas | null | null | null | UAI-P-2011-PG-11-18 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The First-Order Variable Elimination (FOVE) algorithm allows exact inference
to be applied directly to probabilistic relational models, and has proven to be
vastly superior to the application of standard inference methods on a grounded
propositional model. Still, FOVE operators can be applied under restricted
conditions, often forcing one to resort to propositional inference. This paper
aims to extend the applicability of FOVE by providing two new model conversion
operators: the first and the primary is joint formula conversion and the second
is just-different counting conversion. These new operations allow efficient
inference methods to be applied directly on relational models, where no
existing efficient method could be applied hitherto. In addition, aided by
these capabilities, we show how to adapt FOVE to provide exact solutions to
Maximum Expected Utility (MEU) queries over relational models for decision
under uncertainty. Experimental evaluations show our algorithms to provide
significant speedup over the alternatives.
| [
{
"version": "v1",
"created": "Tue, 14 Feb 2012 16:41:17 GMT"
}
] | 1,329,696,000,000 | [
[
"Apsel",
"Udi",
""
],
[
"Brafman",
"Ronen I.",
""
]
] |
1202.3699 | John Asmuth | John Asmuth, Michael L. Littman | Learning is planning: near Bayes-optimal reinforcement learning via
Monte-Carlo tree search | null | null | null | UAI-P-2011-PG-19-26 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bayes-optimal behavior, while well-defined, is often difficult to achieve.
Recent advances in the use of Monte-Carlo tree search (MCTS) have shown that it
is possible to act near-optimally in Markov Decision Processes (MDPs) with very
large or infinite state spaces. Bayes-optimal behavior in an unknown MDP is
equivalent to optimal behavior in the known belief-space MDP, although the size
of this belief-space MDP grows exponentially with the amount of history
retained, and is potentially infinite. We show how an agent can use one
particular MCTS algorithm, Forward Search Sparse Sampling (FSSS), in an
efficient way to act nearly Bayes-optimally for all but a polynomial number of
steps, assuming that FSSS can be used to act efficiently in any possible
underlying MDP.
| [
{
"version": "v1",
"created": "Tue, 14 Feb 2012 16:41:17 GMT"
}
] | 1,329,696,000,000 | [
[
"Asmuth",
"John",
""
],
[
"Littman",
"Michael L.",
""
]
] |
1202.3707 | Shaunak Chatterjee | Shaunak Chatterjee, Stuart Russell | A temporally abstracted Viterbi algorithm | null | null | null | UAI-P-2011-PG-96-104 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Hierarchical problem abstraction, when applicable, may offer exponential
reductions in computational complexity. Previous work on coarse-to-fine dynamic
programming (CFDP) has demonstrated this possibility using state abstraction to
speed up the Viterbi algorithm. In this paper, we show how to apply temporal
abstraction to the Viterbi problem. Our algorithm uses bounds derived from
analysis of coarse timescales to prune large parts of the state trellis at
finer timescales. We demonstrate improvements of several orders of magnitude
over the standard Viterbi algorithm, as well as significant speedups over CFDP,
for problems whose state variables evolve at widely differing rates.
| [
{
"version": "v1",
"created": "Tue, 14 Feb 2012 16:41:17 GMT"
}
] | 1,329,696,000,000 | [
[
"Chatterjee",
"Shaunak",
""
],
[
"Russell",
"Stuart",
""
]
] |
1202.3709 | Arthur Choi | Arthur Choi, Khaled S. Refaat, Adnan Darwiche | EDML: A Method for Learning Parameters in Bayesian Networks | null | null | null | UAI-P-2011-PG-115-124 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a method called EDML for learning MAP parameters in binary
Bayesian networks under incomplete data. The method assumes Beta priors and can
be used to learn maximum likelihood parameters when the priors are
uninformative. EDML exhibits interesting behaviors, especially when compared to
EM. We introduce EDML, explain its origin, and study some of its properties
both analytically and empirically.
| [
{
"version": "v1",
"created": "Tue, 14 Feb 2012 16:41:17 GMT"
}
] | 1,329,696,000,000 | [
[
"Choi",
"Arthur",
""
],
[
"Refaat",
"Khaled S.",
""
],
[
"Darwiche",
"Adnan",
""
]
] |
1202.3711 | Tom Claassen | Tom Claassen, Tom Heskes | A Logical Characterization of Constraint-Based Causal Discovery | null | null | null | UAI-P-2011-PG-135-144 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a novel approach to constraint-based causal discovery, that takes
the form of straightforward logical inference, applied to a list of simple,
logical statements about causal relations that are derived directly from
observed (in)dependencies. It is both sound and complete, in the sense that all
invariant features of the corresponding partial ancestral graph (PAG) are
identified, even in the presence of latent variables and selection bias. The
approach shows that every identifiable causal relation corresponds to one of
just two fundamental forms. More importantly, as the basic building blocks of
the method do not rely on the detailed (graphical) structure of the
corresponding PAG, it opens up a range of new opportunities, including more
robust inference, detailed accountability, and application to large models.
| [
{
"version": "v1",
"created": "Tue, 14 Feb 2012 16:41:17 GMT"
}
] | 1,329,696,000,000 | [
[
"Claassen",
"Tom",
""
],
[
"Heskes",
"Tom",
""
]
] |
1202.3713 | James Cussens | James Cussens | Bayesian network learning with cutting planes | null | null | null | UAI-P-2011-PG-153-160 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The problem of learning the structure of Bayesian networks from complete
discrete data with a limit on parent set size is considered. Learning is cast
explicitly as an optimisation problem where the goal is to find a BN structure
which maximises log marginal likelihood (BDe score). Integer programming,
specifically the SCIP framework, is used to solve this optimisation problem.
Acyclicity constraints are added to the integer program (IP) during solving in
the form of cutting planes. Finding good cutting planes is the key to the
success of the approach -the search for such cutting planes is effected using a
sub-IP. Results show that this is a particularly fast method for exact BN
learning.
| [
{
"version": "v1",
"created": "Tue, 14 Feb 2012 16:41:17 GMT"
}
] | 1,329,696,000,000 | [
[
"Cussens",
"James",
""
]
] |
1202.3718 | Helene Fargier | Helene Fargier, Nahla Ben Amor, Wided Guezguez | On the Complexity of Decision Making in Possibilistic Decision Trees | null | null | null | UAI-P-2011-PG-203-210 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | When the information about uncertainty cannot be quantified in a simple,
probabilistic way, the topic of possibilistic decision theory is often a
natural one to consider. The development of possibilistic decision theory has
lead to a series of possibilistic criteria, e.g pessimistic possibilistic
qualitative utility, possibilistic likely dominance, binary possibilistic
utility and possibilistic Choquet integrals. This paper focuses on sequential
decision making in possibilistic decision trees. It proposes a complexity study
of the problem of finding an optimal strategy depending on the monotonicity
property of the optimization criteria which allows the application of dynamic
programming that offers a polytime reduction of the decision problem. It also
shows that possibilistic Choquet integrals do not satisfy this property, and
that in this case the optimization problem is NP - hard.
| [
{
"version": "v1",
"created": "Tue, 14 Feb 2012 16:41:17 GMT"
}
] | 1,329,696,000,000 | [
[
"Fargier",
"Helene",
""
],
[
"Amor",
"Nahla Ben",
""
],
[
"Guezguez",
"Wided",
""
]
] |
1202.3719 | Daan Fierens | Daan Fierens, Guy Van den Broeck, Ingo Thon, Bernd Gutmann, Luc De
Raedt | Inference in Probabilistic Logic Programs using Weighted CNF's | null | null | null | UAI-P-2011-PG-211-220 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Probabilistic logic programs are logic programs in which some of the facts
are annotated with probabilities. Several classical probabilistic inference
tasks (such as MAP and computing marginals) have not yet received a lot of
attention for this formalism. The contribution of this paper is that we develop
efficient inference algorithms for these tasks. This is based on a conversion
of the probabilistic logic program and the query and evidence to a weighted CNF
formula. This allows us to reduce the inference tasks to well-studied tasks
such as weighted model counting. To solve such tasks, we employ
state-of-the-art methods. We consider multiple methods for the conversion of
the programs as well as for inference on the weighted CNF. The resulting
approach is evaluated experimentally and shown to improve upon the
state-of-the-art in probabilistic logic programming.
| [
{
"version": "v1",
"created": "Tue, 14 Feb 2012 16:41:17 GMT"
}
] | 1,329,696,000,000 | [
[
"Fierens",
"Daan",
""
],
[
"Broeck",
"Guy Van den",
""
],
[
"Thon",
"Ingo",
""
],
[
"Gutmann",
"Bernd",
""
],
[
"De Raedt",
"Luc",
""
]
] |
1202.3721 | Phan H. Giang | Phan H. Giang | Dynamic consistency and decision making under vacuous belief | null | null | null | UAI-P-2011-PG-230-237 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The ideas about decision making under ignorance in economics are combined
with the ideas about uncertainty representation in computer science. The
combination sheds new light on the question of how artificial agents can act in
a dynamically consistent manner. The notion of sequential consistency is
formalized by adapting the law of iterated expectation for plausibility
measures. The necessary and sufficient condition for a certainty equivalence
operator for Nehring-Puppe's preference to be sequentially consistent is given.
This result sheds light on the models of decision making under uncertainty.
| [
{
"version": "v1",
"created": "Tue, 14 Feb 2012 16:41:17 GMT"
}
] | 1,329,696,000,000 | [
[
"Giang",
"Phan H.",
""
]
] |
1202.3723 | Vibhav Gogate | Vibhav Gogate, Pedro Domingos | Approximation by Quantization | null | null | null | UAI-P-2011-PG-247-255 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Inference in graphical models consists of repeatedly multiplying and summing
out potentials. It is generally intractable because the derived potentials
obtained in this way can be exponentially large. Approximate inference
techniques such as belief propagation and variational methods combat this by
simplifying the derived potentials, typically by dropping variables from them.
We propose an alternate method for simplifying potentials: quantizing their
values. Quantization causes different states of a potential to have the same
value, and therefore introduces context-specific independencies that can be
exploited to represent the potential more compactly. We use algebraic decision
diagrams (ADDs) to do this efficiently. We apply quantization and ADD reduction
to variable elimination and junction tree propagation, yielding a family of
bounded approximate inference schemes. Our experimental tests show that our new
schemes significantly outperform state-of-the-art approaches on many benchmark
instances.
| [
{
"version": "v1",
"created": "Tue, 14 Feb 2012 16:41:17 GMT"
}
] | 1,329,696,000,000 | [
[
"Gogate",
"Vibhav",
""
],
[
"Domingos",
"Pedro",
""
]
] |
1202.3724 | Vibhav Gogate | Vibhav Gogate, Pedro Domingos | Probabilistic Theorem Proving | null | null | null | UAI-P-2011-PG-256-265 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many representation schemes combining first-order logic and probability have
been proposed in recent years. Progress in unifying logical and probabilistic
inference has been slower. Existing methods are mainly variants of lifted
variable elimination and belief propagation, neither of which take logical
structure into account. We propose the first method that has the full power of
both graphical model inference and first-order theorem proving (in finite
domains with Herbrand interpretations). We first define probabilistic theorem
proving, their generalization, as the problem of computing the probability of a
logical formula given the probabilities or weights of a set of formulas. We
then show how this can be reduced to the problem of lifted weighted model
counting, and develop an efficient algorithm for the latter. We prove the
correctness of this algorithm, investigate its properties, and show how it
generalizes previous approaches. Experiments show that it greatly outperforms
lifted variable elimination when logical structure is present. Finally, we
propose an algorithm for approximate probabilistic theorem proving, and show
that it can greatly outperform lifted belief propagation.
| [
{
"version": "v1",
"created": "Tue, 14 Feb 2012 16:41:17 GMT"
}
] | 1,329,696,000,000 | [
[
"Gogate",
"Vibhav",
""
],
[
"Domingos",
"Pedro",
""
]
] |
1202.3728 | Hannaneh Hajishirzi | Hannaneh Hajishirzi, Julia Hockenmaier, Erik T. Mueller, Eyal Amir | Reasoning about RoboCup Soccer Narratives | null | null | null | UAI-P-2011-PG-291-300 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents an approach for learning to translate simple narratives,
i.e., texts (sequences of sentences) describing dynamic systems, into coherent
sequences of events without the need for labeled training data. Our approach
incorporates domain knowledge in the form of preconditions and effects of
events, and we show that it outperforms state-of-the-art supervised learning
systems on the task of reconstructing RoboCup soccer games from their
commentaries.
| [
{
"version": "v1",
"created": "Tue, 14 Feb 2012 16:41:17 GMT"
}
] | 1,329,696,000,000 | [
[
"Hajishirzi",
"Hannaneh",
""
],
[
"Hockenmaier",
"Julia",
""
],
[
"Mueller",
"Erik T.",
""
],
[
"Amir",
"Eyal",
""
]
] |
1202.3729 | Eric A. Hansen | Eric A. Hansen | Suboptimality Bounds for Stochastic Shortest Path Problems | null | null | null | UAI-P-2011-PG-301-310 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider how to use the Bellman residual of the dynamic programming
operator to compute suboptimality bounds for solutions to stochastic shortest
path problems. Such bounds have been previously established only in the special
case that "all policies are proper," in which case the dynamic programming
operator is known to be a contraction, and have been shown to be easily
computable only in the more limited special case of discounting. Under the
condition that transition costs are positive, we show that suboptimality bounds
can be easily computed even when not all policies are proper. In the general
case when there are no restrictions on transition costs, the analysis is more
complex. But we present preliminary results that show such bounds are possible.
| [
{
"version": "v1",
"created": "Tue, 14 Feb 2012 16:41:17 GMT"
}
] | 1,329,696,000,000 | [
[
"Hansen",
"Eric A.",
""
]
] |
1202.3740 | Minyi Li | Minyi Li, Quoc Bao Vo, Ryszard Kowalczyk | An Efficient Protocol for Negotiation over Combinatorial Domains with
Incomplete Information | null | null | null | UAI-P-2011-PG-436-444 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the problem of agent-based negotiation in combinatorial domains. It
is difficult to reach optimal agreements in bilateral or multi-lateral
negotiations when the agents' preferences for the possible alternatives are not
common knowledge. Self-interested agents often end up negotiating inefficient
agreements in such situations. In this paper, we present a protocol for
negotiation in combinatorial domains which can lead rational agents to reach
optimal agreements under incomplete information setting. Our proposed protocol
enables the negotiating agents to identify efficient solutions using
distributed search that visits only a small subspace of the whole outcome
space. Moreover, the proposed protocol is sufficiently general that it is
applicable to most preference representation models in combinatorial domains.
We also present results of experiments that demonstrate the feasibility and
computational efficiency of our approach.
| [
{
"version": "v1",
"created": "Tue, 14 Feb 2012 16:41:17 GMT"
}
] | 1,329,696,000,000 | [
[
"Li",
"Minyi",
""
],
[
"Vo",
"Quoc Bao",
""
],
[
"Kowalczyk",
"Ryszard",
""
]
] |
1202.3741 | Shiau Hong Lim | Shiau Hong Lim, Peter Auer | Noisy Search with Comparative Feedback | null | null | null | UAI-P-2011-PG-445-452 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present theoretical results in terms of lower and upper bounds on the
query complexity of noisy search with comparative feedback. In this search
model, the noise in the feedback depends on the distance between query points
and the search target. Consequently, the error probability in the feedback is
not fixed but varies for the queries posed by the search algorithm. Our results
show that a target out of n items can be found in O(log n) queries. We also
show the surprising result that for k possible answers per query, the speedup
is not log k (as for k-ary search) but only log log k in some cases.
| [
{
"version": "v1",
"created": "Tue, 14 Feb 2012 16:41:17 GMT"
}
] | 1,329,696,000,000 | [
[
"Lim",
"Shiau Hong",
""
],
[
"Auer",
"Peter",
""
]
] |
1202.3743 | Jianbing Ma | Jianbing Ma, Weiru Liu, Paul Miller | Belief change with noisy sensing in the situation calculus | null | null | null | UAI-P-2011-PG-471-478 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Situation calculus has been applied widely in artificial intelligence to
model and reason about actions and changes in dynamic systems. Since actions
carried out by agents will cause constant changes of the agents' beliefs, how
to manage these changes is a very important issue. Shapiro et al. [22] is one
of the studies that considered this issue. However, in this framework, the
problem of noisy sensing, which often presents in real-world applications, is
not considered. As a consequence, noisy sensing actions in this framework will
lead to an agent facing inconsistent situation and subsequently the agent
cannot proceed further. In this paper, we investigate how noisy sensing actions
can be handled in iterated belief change within the situation calculus
formalism. We extend the framework proposed in [22] with the capability of
managing noisy sensings. We demonstrate that an agent can still detect the
actual situation when the ratio of noisy sensing actions vs. accurate sensing
actions is limited. We prove that our framework subsumes the iterated belief
change strategy in [22] when all sensing actions are accurate. Furthermore, we
prove that our framework can adequately handle belief introspection, mistaken
beliefs, belief revision and belief update even with noisy sensing, as done in
[22] with accurate sensing actions only.
| [
{
"version": "v1",
"created": "Tue, 14 Feb 2012 16:41:17 GMT"
}
] | 1,329,696,000,000 | [
[
"Ma",
"Jianbing",
""
],
[
"Liu",
"Weiru",
""
],
[
"Miller",
"Paul",
""
]
] |
1202.3744 | Brandon Malone | Brandon Malone, Changhe Yuan, Eric A. Hansen, Susan Bridges | Improving the Scalability of Optimal Bayesian Network Learning with
External-Memory Frontier Breadth-First Branch and Bound Search | null | null | null | UAI-P-2011-PG-479-488 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Previous work has shown that the problem of learning the optimal structure of
a Bayesian network can be formulated as a shortest path finding problem in a
graph and solved using A* search. In this paper, we improve the scalability of
this approach by developing a memory-efficient heuristic search algorithm for
learning the structure of a Bayesian network. Instead of using A*, we propose a
frontier breadth-first branch and bound search that leverages the layered
structure of the search graph of this problem so that no more than two layers
of the graph, plus solution reconstruction information, need to be stored in
memory at a time. To further improve scalability, the algorithm stores most of
the graph in external memory, such as hard disk, when it does not fit in RAM.
Experimental results show that the resulting algorithm solves significantly
larger problems than the current state of the art.
| [
{
"version": "v1",
"created": "Tue, 14 Feb 2012 16:41:17 GMT"
}
] | 1,329,696,000,000 | [
[
"Malone",
"Brandon",
""
],
[
"Yuan",
"Changhe",
""
],
[
"Hansen",
"Eric A.",
""
],
[
"Bridges",
"Susan",
""
]
] |
1202.3745 | Radu Marinescu | Radu Marinescu, Nic Wilson | Order-of-Magnitude Influence Diagrams | null | null | null | UAI-P-2011-PG-489-496 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we develop a qualitative theory of influence diagrams that can
be used to model and solve sequential decision making tasks when only
qualitative (or imprecise) information is available. Our approach is based on
an order-of-magnitude approximation of both probabilities and utilities and
allows for specifying partially ordered preferences via sets of utility values.
We also propose a dedicated variable elimination algorithm that can be applied
for solving order-of-magnitude influence diagrams.
| [
{
"version": "v1",
"created": "Tue, 14 Feb 2012 16:41:17 GMT"
}
] | 1,329,696,000,000 | [
[
"Marinescu",
"Radu",
""
],
[
"Wilson",
"Nic",
""
]
] |
1202.3749 | Hala Mostafa | Hala Mostafa, Victor Lesser | Compact Mathematical Programs For DEC-MDPs With Structured Agent
Interactions | null | null | null | UAI-P-2011-PG-523-530 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To deal with the prohibitive complexity of calculating policies in
Decentralized MDPs, researchers have proposed models that exploit structured
agent interactions. Settings where most agent actions are independent except
for few actions that affect the transitions and/or rewards of other agents can
be modeled using Event-Driven Interactions with Complex Rewards (EDI-CR).
Finding the optimal joint policy can be formulated as an optimization problem.
However, existing formulations are too verbose and/or lack optimality
guarantees. We propose a compact Mixed Integer Linear Program formulation of
EDI-CR instances. The key insight is that most action sequences of a group of
agents have the same effect on a given agent. This allows us to treat these
sequences similarly and use fewer variables. Experiments show that our
formulation is more compact and leads to faster solution times and better
solutions than existing formulations.
| [
{
"version": "v1",
"created": "Tue, 14 Feb 2012 16:41:17 GMT"
}
] | 1,329,696,000,000 | [
[
"Mostafa",
"Hala",
""
],
[
"Lesser",
"Victor",
""
]
] |
1202.3754 | Eunsoo Oh | Eunsoo Oh, Kee-Eung Kim | A Geometric Traversal Algorithm for Reward-Uncertain MDPs | null | null | null | UAI-P-2011-PG-565-572 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Markov decision processes (MDPs) are widely used in modeling decision making
problems in stochastic environments. However, precise specification of the
reward functions in MDPs is often very difficult. Recent approaches have
focused on computing an optimal policy based on the minimax regret criterion
for obtaining a robust policy under uncertainty in the reward function. One of
the core tasks in computing the minimax regret policy is to obtain the set of
all policies that can be optimal for some candidate reward function. In this
paper, we propose an efficient algorithm that exploits the geometric properties
of the reward function associated with the policies. We also present an
approximate version of the method for further speed up. We experimentally
demonstrate that our algorithm improves the performance by orders of magnitude.
| [
{
"version": "v1",
"created": "Tue, 14 Feb 2012 16:41:17 GMT"
}
] | 1,329,696,000,000 | [
[
"Oh",
"Eunsoo",
""
],
[
"Kim",
"Kee-Eung",
""
]
] |
1202.3759 | Gungor Polatkan | Gungor Polatkan, Oncel Tuzel | Compressed Inference for Probabilistic Sequential Models | null | null | null | UAI-P-2011-PG-609-618 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Hidden Markov models (HMMs) and conditional random fields (CRFs) are two
popular techniques for modeling sequential data. Inference algorithms designed
over CRFs and HMMs allow estimation of the state sequence given the
observations. In several applications, estimation of the state sequence is not
the end goal; instead the goal is to compute some function of it. In such
scenarios, estimating the state sequence by conventional inference techniques,
followed by computing the functional mapping from the estimate is not
necessarily optimal. A more formal approach is to directly infer the final
outcome from the observations. In particular, we consider the specific
instantiation of the problem where the goal is to find the state trajectories
without exact transition points and derive a novel polynomial time inference
algorithm that outperforms vanilla inference techniques. We show that this
particular problem arises commonly in many disparate applications and present
experiments on three of them: (1) Toy robot tracking; (2) Single stroke
character recognition; (3) Handwritten word recognition.
| [
{
"version": "v1",
"created": "Tue, 14 Feb 2012 16:41:17 GMT"
}
] | 1,329,696,000,000 | [
[
"Polatkan",
"Gungor",
""
],
[
"Tuzel",
"Oncel",
""
]
] |
1202.3762 | Scott Sanner | Scott Sanner, Karina Valdivia Delgado, Leliane Nunes de Barros | Symbolic Dynamic Programming for Discrete and Continuous State MDPs | null | null | null | UAI-P-2011-PG-643-652 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many real-world decision-theoretic planning problems can be naturally modeled
with discrete and continuous state Markov decision processes (DC-MDPs). While
previous work has addressed automated decision-theoretic planning for DCMDPs,
optimal solutions have only been defined so far for limited settings, e.g.,
DC-MDPs having hyper-rectangular piecewise linear value functions. In this
work, we extend symbolic dynamic programming (SDP) techniques to provide
optimal solutions for a vastly expanded class of DCMDPs. To address the
inherent combinatorial aspects of SDP, we introduce the XADD - a continuous
variable extension of the algebraic decision diagram (ADD) - that maintains
compact representations of the exact value function. Empirically, we
demonstrate an implementation of SDP with XADDs on various DC-MDPs, showing the
first optimal automated solutions to DCMDPs with linear and nonlinear piecewise
partitioned value functions and showing the advantages of constraint-based
pruning for XADDs.
| [
{
"version": "v1",
"created": "Tue, 14 Feb 2012 16:41:17 GMT"
}
] | 1,329,696,000,000 | [
[
"Sanner",
"Scott",
""
],
[
"Delgado",
"Karina Valdivia",
""
],
[
"de Barros",
"Leliane Nunes",
""
]
] |
1202.3764 | Johannes Textor | Johannes Textor, Maciej Liskiewicz | Adjustment Criteria in Causal Diagrams: An Algorithmic Perspective | null | null | null | UAI-P-2011-PG-681-688 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Identifying and controlling bias is a key problem in empirical sciences.
Causal diagram theory provides graphical criteria for deciding whether and how
causal effects can be identified from observed (nonexperimental) data by
covariate adjustment. Here we prove equivalences between existing as well as
new criteria for adjustment and we provide a new simplified but still
equivalent notion of d-separation. These lead to efficient algorithms for two
important tasks in causal diagram analysis: (1) listing minimal covariate
adjustments (with polynomial delay); and (2) identifying the subdiagram
involved in biasing paths (in linear time). Our results improve upon existing
exponential-time solutions for these problems, enabling users to assess the
effects of covariate adjustment on diagrams with tens to hundreds of variables
interactively in real time.
| [
{
"version": "v1",
"created": "Tue, 14 Feb 2012 16:41:17 GMT"
}
] | 1,329,696,000,000 | [
[
"Textor",
"Johannes",
""
],
[
"Liskiewicz",
"Maciej",
""
]
] |
1202.3767 | Joop van de Ven | Joop van de Ven, Fabio Ramos | Distributed Anytime MAP Inference | null | null | null | UAI-P-2011-PG-708-716 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a distributed anytime algorithm for performing MAP inference in
graphical models. The problem is formulated as a linear programming relaxation
over the edges of a graph. The resulting program has a constraint structure
that allows application of the Dantzig-Wolfe decomposition principle.
Subprograms are defined over individual edges and can be computed in a
distributed manner. This accommodates solutions to graphs whose state space
does not fit in memory. The decomposition master program is guaranteed to
compute the optimal solution in a finite number of iterations, while the
solution converges monotonically with each iteration. Formulating the MAP
inference problem as a linear program allows additional (global) constraints to
be defined; something not possible with message passing algorithms.
Experimental results show that our algorithm's solution quality outperforms
most current algorithms and it scales well to large problems.
| [
{
"version": "v1",
"created": "Tue, 14 Feb 2012 16:41:17 GMT"
}
] | 1,329,696,000,000 | [
[
"van de Ven",
"Joop",
""
],
[
"Ramos",
"Fabio",
""
]
] |
1202.3773 | Haohai Yu | Haohai Yu, Robert A. van Engelen | Measuring the Hardness of Stochastic Sampling on Bayesian Networks with
Deterministic Causalities: the k-Test | null | null | null | UAI-P-2011-PG-786-795 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Approximate Bayesian inference is NP-hard. Dagum and Luby defined the Local
Variance Bound (LVB) to measure the approximation hardness of Bayesian
inference on Bayesian networks, assuming the networks model strictly positive
joint probability distributions, i.e. zero probabilities are not permitted.
This paper introduces the k-test to measure the approximation hardness of
inference on Bayesian networks with deterministic causalities in the
probability distribution, i.e. when zero conditional probabilities are
permitted. Approximation by stochastic sampling is a widely-used inference
method that is known to suffer from inefficiencies due to sample rejection. The
k-test predicts when rejection rates of stochastic sampling a Bayesian network
will be low, modest, high, or when sampling is intractable.
| [
{
"version": "v1",
"created": "Tue, 14 Feb 2012 16:41:17 GMT"
}
] | 1,329,696,000,000 | [
[
"Yu",
"Haohai",
""
],
[
"van Engelen",
"Robert A.",
""
]
] |
1202.3887 | Hamid Salimi | Hamid Salimi, Davar Giveki, Mohammad Ali Soltanshahi, Javad Hatami | Extended Mixture of MLP Experts by Hybrid of Conjugate Gradient Method
and Modified Cuckoo Search | 13 pages, 2 figures | International Journal of Artificial Intelligence & Applications
(IJAIA), Vol.3, No.1, January 2012 | null | null | cs.AI | http://creativecommons.org/licenses/by/3.0/ | This paper investigates a new method for improving the learning algorithm of
Mixture of Experts (ME) model using a hybrid of Modified Cuckoo Search (MCS)
and Conjugate Gradient (CG) as a second order optimization technique. The CG
technique is combined with Back-Propagation (BP) algorithm to yield a much more
efficient learning algorithm for ME structure. In addition, the experts and
gating networks in enhanced model are replaced by CG based Multi-Layer
Perceptrons (MLPs) to provide faster and more accurate learning. The CG is
considerably depends on initial weights of connections of Artificial Neural
Network (ANN), so, a metaheuristic algorithm, the so-called Modified Cuckoo
Search is applied in order to select the optimal weights. The performance of
proposed method is compared with Gradient Decent Based ME (GDME) and Conjugate
Gradient Based ME (CGME) in classification and regression problems. The
experimental results show that hybrid MSC and CG based ME (MCS-CGME) has faster
convergence and better performance in utilized benchmark data sets.
| [
{
"version": "v1",
"created": "Fri, 17 Feb 2012 11:49:56 GMT"
}
] | 1,329,696,000,000 | [
[
"Salimi",
"Hamid",
""
],
[
"Giveki",
"Davar",
""
],
[
"Soltanshahi",
"Mohammad Ali",
""
],
[
"Hatami",
"Javad",
""
]
] |
1202.4190 | Feng Lin | Feng Lin, Robert C. Qiu, Zhen Hu, Shujie Hou, James P. Browning,
Michael C. Wicks | Generalized FMD Detection for Spectrum Sensing Under Low Signal-to-Noise
Ratio | 4 pages, 1 figure, 1 table | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/3.0/ | Spectrum sensing is a fundamental problem in cognitive radio. We propose a
function of covariance matrix based detection algorithm for spectrum sensing in
cognitive radio network. Monotonically increasing property of function of
matrix involving trace operation is utilized as the cornerstone for this
algorithm. The advantage of proposed algorithm is it works under extremely low
signal-to-noise ratio, like lower than -30 dB with limited sample data.
Theoretical analysis of threshold setting for the algorithm is discussed. A
performance comparison between the proposed algorithm and other
state-of-the-art methods is provided, by the simulation on captured digital
television (DTV) signal.
| [
{
"version": "v1",
"created": "Sun, 19 Feb 2012 21:50:58 GMT"
}
] | 1,329,782,400,000 | [
[
"Lin",
"Feng",
""
],
[
"Qiu",
"Robert C.",
""
],
[
"Hu",
"Zhen",
""
],
[
"Hou",
"Shujie",
""
],
[
"Browning",
"James P.",
""
],
[
"Wicks",
"Michael C.",
""
]
] |
1202.6009 | Josep Domingo-Ferrer | Josep Domingo-Ferrer | Marginality: a numerical mapping for enhanced treatment of nominal and
hierarchical attributes | 12 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The purpose of statistical disclosure control (SDC) of microdata, a.k.a. data
anonymization or privacy-preserving data mining, is to publish data sets
containing the answers of individual respondents in such a way that the
respondents corresponding to the released records cannot be re-identified and
the released data are analytically useful. SDC methods are either based on
masking the original data, generating synthetic versions of them or creating
hybrid versions by combining original and synthetic data. The choice of SDC
methods for categorical data, especially nominal data, is much smaller than the
choice of methods for numerical data. We mitigate this problem by introducing a
numerical mapping for hierarchical nominal data which allows computing means,
variances and covariances on them.
| [
{
"version": "v1",
"created": "Mon, 27 Feb 2012 17:37:20 GMT"
}
] | 1,330,387,200,000 | [
[
"Domingo-Ferrer",
"Josep",
""
]
] |
1202.6153 | Marcus Hutter | Marcus Hutter | One Decade of Universal Artificial Intelligence | 20 LaTeX pages | In Theoretical Foundations of Artificial General Intelligence,
Vol.4 (2012) pages 67--88 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The first decade of this century has seen the nascency of the first
mathematical theory of general artificial intelligence. This theory of
Universal Artificial Intelligence (UAI) has made significant contributions to
many theoretical, philosophical, and practical AI questions. In a series of
papers culminating in book (Hutter, 2005), an exciting sound and complete
mathematical model for a super intelligent agent (AIXI) has been developed and
rigorously analyzed. While nowadays most AI researchers avoid discussing
intelligence, the award-winning PhD thesis (Legg, 2008) provided the
philosophical embedding and investigated the UAI-based universal measure of
rational intelligence, which is formal, objective and non-anthropocentric.
Recently, effective approximations of AIXI have been derived and experimentally
investigated in JAIR paper (Veness et al. 2011). This practical breakthrough
has resulted in some impressive applications, finally muting earlier critique
that UAI is only a theory. For the first time, without providing any domain
knowledge, the same agent is able to self-adapt to a diverse range of
interactive environments. For instance, AIXI is able to learn from scratch to
play TicTacToe, Pacman, Kuhn Poker, and other games by trial and error, without
even providing the rules of the games.
These achievements give new hope that the grand goal of Artificial General
Intelligence is not elusive.
This article provides an informal overview of UAI in context. It attempts to
gently introduce a very theoretical, formal, and mathematical subject, and
discusses philosophical and technical ingredients, traits of intelligence, some
social questions, and the past and future of UAI.
| [
{
"version": "v1",
"created": "Tue, 28 Feb 2012 09:19:32 GMT"
}
] | 1,368,748,800,000 | [
[
"Hutter",
"Marcus",
""
]
] |
1202.6386 | Shiwali Mohan | Shiwali Mohan and John E. Laird | Relational Reinforcement Learning in Infinite Mario | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Relational representations in reinforcement learning allow for the use of
structural information like the presence of objects and relationships between
them in the description of value functions. Through this paper, we show that
such representations allow for the inclusion of background knowledge that
qualitatively describes a state and can be used to design agents that
demonstrate learning behavior in domains with large state and actions spaces
such as computer games.
| [
{
"version": "v1",
"created": "Tue, 28 Feb 2012 21:36:22 GMT"
}
] | 1,330,560,000,000 | [
[
"Mohan",
"Shiwali",
""
],
[
"Laird",
"John E.",
""
]
] |
1203.1021 | Ahmed Maalel | Ahmed Maalel, Habib Hadj mabrouk, Lassad Mejri and Henda Hajjami Ben
Ghezela | Development of an Ontology to Assist the Modeling of Accident Scenarii
"Application on Railroad Transport " | 7 pages, 9 figures, Journal of Computing (ISSN 2151-9617); Journal of
Computing, Volume 3, Issue 7, July 2011 | J. of Computing. 3. 7. (2011) 1-8 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In a world where communication and information sharing are at the heart of
our business, the terminology needs are most pressing. It has become imperative
to identify the terms used and defined in a consensual and coherent way while
preserving linguistic diversity. To streamline and strengthen the process of
acquisition, representation and exploitation of scenarii of train accidents, it
is necessary to harmonize and standardize the terminology used by players in
the security field. The research aims to significantly improve analytical
activities and operations of the various safety studies, by tracking the error
in system, hardware, software and human. This paper presents the contribution
of ontology to modeling scenarii for rail accidents through a knowledge model
based on a generic ontology and domain ontology. After a detailed presentation
of the state of the art material, this article presents the first results of
the developed model.
| [
{
"version": "v1",
"created": "Mon, 5 Mar 2012 19:45:43 GMT"
}
] | 1,331,078,400,000 | [
[
"Maalel",
"Ahmed",
""
],
[
"mabrouk",
"Habib Hadj",
""
],
[
"Mejri",
"Lassad",
""
],
[
"Ghezela",
"Henda Hajjami Ben",
""
]
] |
1203.1095 | Guido Tack | Tom Schrijvers, Guido Tack, Pieter Wuille, Horst Samulowitz, Peter J.
Stuckey | Search Combinators | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The ability to model search in a constraint solver can be an essential asset
for solving combinatorial problems. However, existing infrastructure for
defining search heuristics is often inadequate. Either modeling capabilities
are extremely limited or users are faced with a general-purpose programming
language whose features are not tailored towards writing search heuristics. As
a result, major improvements in performance may remain unexplored.
This article introduces search combinators, a lightweight and
solver-independent method that bridges the gap between a conceptually simple
modeling language for search (high-level, functional and naturally
compositional) and an efficient implementation (low-level, imperative and
highly non-modular). By allowing the user to define application-tailored search
strategies from a small set of primitives, search combinators effectively
provide a rich domain-specific language (DSL) for modeling search to the user.
Remarkably, this DSL comes at a low implementation cost to the developer of a
constraint solver.
The article discusses two modular implementation approaches and shows, by
empirical evaluation, that search combinators can be implemented without
overhead compared to a native, direct implementation in a constraint solver.
| [
{
"version": "v1",
"created": "Tue, 6 Mar 2012 03:59:34 GMT"
}
] | 1,331,078,400,000 | [
[
"Schrijvers",
"Tom",
""
],
[
"Tack",
"Guido",
""
],
[
"Wuille",
"Pieter",
""
],
[
"Samulowitz",
"Horst",
""
],
[
"Stuckey",
"Peter J.",
""
]
] |
1203.1882 | Ganti Meenakshi | G.Meenakshi | Multi source feedback based performance appraisal system using Fuzzy
logic decision support system | 16 pages | null | 10.5121/ijsc.2012.3108 | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/3.0/ | In Multi-Source Feedback or 360 Degree Feedback, data on the performance of
an individual are collected systematically from a number of stakeholders and
are used for improving performance. The 360-Degree Feedback approach provides a
consistent management philosophy meeting the criterion outlined previously. The
360-degree feedback appraisal process describes a human resource methodology
that is frequently used for both employee appraisal and employee development.
Used in employee performance appraisals, the 360-degree feedback methodology is
differentiated from traditional, top-down appraisal methods in which the
supervisor responsible for the appraisal provides the majority of the data.
Instead it seeks to use information gained from other sources to provide a
fuller picture of employees' performances. Similarly, when this technique used
in employee development it augments employees' perceptions of training needs
with those of the people with whom they interact. The 360-degree feedback based
appraisal is a comprehensive method where in the feedback about the employee
comes from all the sources that come into contact with the employee on his/her
job. The respondents for an employee can be her/his peers, managers,
subordinates team members, customers, suppliers and vendors. Hence anyone who
comes into contact with the employee, the 360 degree appraisal has four
components that include self-appraisal, superior's appraisal, subordinate's
appraisal student's appraisal and peer's appraisal .The proposed system is an
attempt to implement the 360 degree feedback based appraisal system in
academics especially engineering colleges.
| [
{
"version": "v1",
"created": "Thu, 8 Mar 2012 18:44:46 GMT"
}
] | 1,331,251,200,000 | [
[
"Meenakshi",
"G.",
""
]
] |
1203.3051 | Nina Narodytska | Nina Narodytska, Toby Walsh and Lirong Xia | Combining Voting Rules Together | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a simple method for combining together voting rules that performs
a run-off between the different winners of each voting rule. We prove that this
combinator has several good properties. For instance, even if just one of the
base voting rules has a desirable property like Condorcet consistency, the
combination inherits this property. In addition, we prove that combining voting
rules together in this way can make finding a manipulation more computationally
difficult. Finally, we study the impact of this combinator on approximation
methods that find close to optimal manipulations.
| [
{
"version": "v1",
"created": "Wed, 14 Mar 2012 11:27:15 GMT"
}
] | 1,331,769,600,000 | [
[
"Narodytska",
"Nina",
""
],
[
"Walsh",
"Toby",
""
],
[
"Xia",
"Lirong",
""
]
] |
1203.3464 | Nimar S. Arora | Nimar S. Arora, Rodrigo de Salvo Braz, Erik B. Sudderth, Stuart
Russell | Gibbs Sampling in Open-Universe Stochastic Languages | Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty
in Artificial Intelligence (UAI2010) | null | null | UAI-P-2010-PG-30-39 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Languages for open-universe probabilistic models (OUPMs) can represent
situations with an unknown number of objects and iden- tity uncertainty. While
such cases arise in a wide range of important real-world appli- cations,
existing general purpose inference methods for OUPMs are far less efficient
than those available for more restricted lan- guages and model classes. This
paper goes some way to remedying this deficit by in- troducing, and proving
correct, a generaliza- tion of Gibbs sampling to partial worlds with possibly
varying model structure. Our ap- proach draws on and extends previous generic
OUPM inference methods, as well as aux- iliary variable samplers for
nonparametric mixture models. It has been implemented for BLOG, a well-known
OUPM language. Combined with compile-time optimizations, the resulting
algorithm yields very substan- tial speedups over existing methods on sev- eral
test cases, and substantially improves the practicality of OUPM languages
generally.
| [
{
"version": "v1",
"created": "Thu, 15 Mar 2012 11:17:56 GMT"
}
] | 1,332,115,200,000 | [
[
"Arora",
"Nimar S.",
""
],
[
"Braz",
"Rodrigo de Salvo",
""
],
[
"Sudderth",
"Erik B.",
""
],
[
"Russell",
"Stuart",
""
]
] |
1203.3465 | Raouia Ayachi | Raouia Ayachi, Nahla Ben Amor, Salem Benferhat, Rolf Haenni | Compiling Possibilistic Networks: Alternative Approaches to
Possibilistic Inference | Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty
in Artificial Intelligence (UAI2010) | null | null | UAI-P-2010-PG-40-47 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Qualitative possibilistic networks, also known as min-based possibilistic
networks, are important tools for handling uncertain information in the
possibility theory frame- work. Despite their importance, only the junction
tree adaptation has been proposed for exact reasoning with such networks. This
paper explores alternative algorithms using compilation techniques. We first
propose possibilistic adaptations of standard compilation-based probabilistic
methods. Then, we develop a new, purely possibilistic, method based on the
transformation of the initial network into a possibilistic base. A comparative
study shows that this latter performs better than the possibilistic adap-
tations of probabilistic methods. This result is also confirmed by experimental
results.
| [
{
"version": "v1",
"created": "Thu, 15 Mar 2012 11:17:56 GMT"
}
] | 1,332,115,200,000 | [
[
"Ayachi",
"Raouia",
""
],
[
"Amor",
"Nahla Ben",
""
],
[
"Benferhat",
"Salem",
""
],
[
"Haenni",
"Rolf",
""
]
] |
1203.3466 | Kim Bauters | Kim Bauters, Steven Schockaert, Martine De Cock, Dirk Vermeir | Possibilistic Answer Set Programming Revisited | Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty
in Artificial Intelligence (UAI2010) | null | null | UAI-P-2010-PG-48-55 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Possibilistic answer set programming (PASP) extends answer set programming
(ASP) by attaching to each rule a degree of certainty. While such an extension
is important from an application point of view, existing semantics are not
well-motivated, and do not always yield intuitive results. To develop a more
suitable semantics, we first introduce a characterization of answer sets of
classical ASP programs in terms of possibilistic logic where an ASP program
specifies a set of constraints on possibility distributions. This
characterization is then naturally generalized to define answer sets of PASP
programs. We furthermore provide a syntactic counterpart, leading to a
possibilistic generalization of the well-known Gelfond-Lifschitz reduct, and we
show how our framework can readily be implemented using standard ASP solvers.
| [
{
"version": "v1",
"created": "Thu, 15 Mar 2012 11:17:56 GMT"
}
] | 1,332,115,200,000 | [
[
"Bauters",
"Kim",
""
],
[
"Schockaert",
"Steven",
""
],
[
"De Cock",
"Martine",
""
],
[
"Vermeir",
"Dirk",
""
]
] |
1203.3467 | Debarun Bhattacharjya | Debarun Bhattacharjya, Ross D. Shachter | Three new sensitivity analysis methods for influence diagrams | Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty
in Artificial Intelligence (UAI2010) | null | null | UAI-P-2010-PG-56-64 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Performing sensitivity analysis for influence diagrams using the decision
circuit framework is particularly convenient, since the partial derivatives
with respect to every parameter are readily available [Bhattacharjya and
Shachter, 2007; 2008]. In this paper we present three non-linear sensitivity
analysis methods that utilize this partial derivative information and therefore
do not require re-evaluating the decision situation multiple times.
Specifically, we show how to efficiently compare strategies in decision
situations, perform sensitivity to risk aversion and compute the value of
perfect hedging [Seyller, 2008].
| [
{
"version": "v1",
"created": "Thu, 15 Mar 2012 11:17:56 GMT"
}
] | 1,332,115,200,000 | [
[
"Bhattacharjya",
"Debarun",
""
],
[
"Shachter",
"Ross D.",
""
]
] |
1203.3469 | Matthias Brocheler | Matthias Brocheler, Lilyana Mihalkova, Lise Getoor | Probabilistic Similarity Logic | Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty
in Artificial Intelligence (UAI2010) | null | null | UAI-P-2010-PG-73-82 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many machine learning applications require the ability to learn from and
reason about noisy multi-relational data. To address this, several effective
representations have been developed that provide both a language for expressing
the structural regularities of a domain, and principled support for
probabilistic inference. In addition to these two aspects, however, many
applications also involve a third aspect-the need to reason about
similarities-which has not been directly supported in existing frameworks. This
paper introduces probabilistic similarity logic (PSL), a general-purpose
framework for joint reasoning about similarity in relational domains that
incorporates probabilistic reasoning about similarities and relational
structure in a principled way. PSL can integrate any existing domain-specific
similarity measures and also supports reasoning about similarities between sets
of entities. We provide efficient inference and learning techniques for PSL and
demonstrate its effectiveness both in common relational tasks and in settings
that require reasoning about similarity.
| [
{
"version": "v1",
"created": "Thu, 15 Mar 2012 11:17:56 GMT"
}
] | 1,332,115,200,000 | [
[
"Brocheler",
"Matthias",
""
],
[
"Mihalkova",
"Lilyana",
""
],
[
"Getoor",
"Lise",
""
]
] |
1203.3470 | Alan S. Carlin | Alan S. Carlin, Nathan Schurr, Janusz Marecki | ALARMS: Alerting and Reasoning Management System for Next Generation
Aircraft Hazards | Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty
in Artificial Intelligence (UAI2010) | null | null | UAI-P-2010-PG-93-100 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Next Generation Air Transportation System will introduce new, advanced
sensor technologies into the cockpit. With the introduction of such systems,
the responsibilities of the pilot are expected to dramatically increase. In the
ALARMS (ALerting And Reasoning Management System) project for NASA, we focus on
a key challenge of this environment, the quick and efficient handling of
aircraft sensor alerts. It is infeasible to alert the pilot on the state of all
subsystems at all times. Furthermore, there is uncertainty as to the true
hazard state despite the evidence of the alerts, and there is uncertainty as to
the effect and duration of actions taken to address these alerts. This paper
reports on the first steps in the construction of an application designed to
handle Next Generation alerts. In ALARMS, we have identified 60 different
aircraft subsystems and 20 different underlying hazards. In this paper, we show
how a Bayesian network can be used to derive the state of the underlying
hazards, based on the sensor input. Then, we propose a framework whereby an
automated system can plan to address these hazards in cooperation with the
pilot, using a Time-Dependent Markov Process (TMDP). Different hazards and
pilot states will call for different alerting automation plans. We demonstrate
this emerging application of Bayesian networks and TMDPs to cockpit automation,
for a use case where a small number of hazards are present, and analyze the
resulting alerting automation policies.
| [
{
"version": "v1",
"created": "Thu, 15 Mar 2012 11:17:56 GMT"
}
] | 1,332,115,200,000 | [
[
"Carlin",
"Alan S.",
""
],
[
"Schurr",
"Nathan",
""
],
[
"Marecki",
"Janusz",
""
]
] |
1203.3473 | Jaesik Choi | Jaesik Choi, Eyal Amir, David J. Hill | Lifted Inference for Relational Continuous Models | Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty
in Artificial Intelligence (UAI2010) | null | null | UAI-P-2010-PG-126-134 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Relational Continuous Models (RCMs) represent joint probability densities
over attributes of objects, when the attributes have continuous domains. With
relational representations, they can model joint probability distributions over
large numbers of variables compactly in a natural way. This paper presents a
new exact lifted inference algorithm for RCMs, thus it scales up to large
models of real world applications. The algorithm applies to Relational Pairwise
Models which are (relational) products of potentials of arity 2. Our algorithm
is unique in two ways. First, it substantially improves the efficiency of
lifted inference with variables of continuous domains. When a relational model
has Gaussian potentials, it takes only linear-time compared to cubic time of
previous methods. Second, it is the first exact inference algorithm which
handles RCMs in a lifted way. The algorithm is illustrated over an example from
econometrics. Experimental results show that our algorithm outperforms both a
groundlevel inference algorithm and an algorithm built with previously-known
lifted methods.
| [
{
"version": "v1",
"created": "Thu, 15 Mar 2012 11:17:56 GMT"
}
] | 1,332,115,200,000 | [
[
"Choi",
"Jaesik",
""
],
[
"Amir",
"Eyal",
""
],
[
"Hill",
"David J.",
""
]
] |
1203.3474 | Gabriel Corona | Gabriel Corona, Francois Charpillet | Distribution over Beliefs for Memory Bounded Dec-POMDP Planning | Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty
in Artificial Intelligence (UAI2010) | null | null | UAI-P-2010-PG-135-142 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a new point-based method for approximate planning in Dec-POMDP
which outperforms the state-of-the-art approaches in terms of solution quality.
It uses a heuristic estimation of the prior probability of beliefs to choose a
bounded number of policy trees: this choice is formulated as a combinatorial
optimisation problem minimising the error induced by pruning.
| [
{
"version": "v1",
"created": "Thu, 15 Mar 2012 11:17:56 GMT"
}
] | 1,332,115,200,000 | [
[
"Corona",
"Gabriel",
""
],
[
"Charpillet",
"Francois",
""
]
] |
1203.3477 | Tom Erez | Tom Erez, William D. Smart | A Scalable Method for Solving High-Dimensional Continuous POMDPs Using
Local Approximation | Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty
in Artificial Intelligence (UAI2010) | null | null | UAI-P-2010-PG-160-167 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Partially-Observable Markov Decision Processes (POMDPs) are typically solved
by finding an approximate global solution to a corresponding belief-MDP. In
this paper, we offer a new planning algorithm for POMDPs with continuous state,
action and observation spaces. Since such domains have an inherent notion of
locality, we can find an approximate solution using local optimization methods.
We parameterize the belief distribution as a Gaussian mixture, and use the
Extended Kalman Filter (EKF) to approximate the belief update. Since the EKF is
a first-order filter, we can marginalize over the observations analytically. By
using feedback control and state estimation during policy execution, we recover
a behavior that is effectively conditioned on incoming observations despite the
unconditioned planning. Local optimization provides no guarantees of global
optimality, but it allows us to tackle domains that are at least an order of
magnitude larger than the current state-of-the-art. We demonstrate the
scalability of our algorithm by considering a simulated hand-eye coordination
domain with 16 continuous state dimensions and 6 continuous action dimensions.
| [
{
"version": "v1",
"created": "Thu, 15 Mar 2012 11:17:56 GMT"
}
] | 1,332,115,200,000 | [
[
"Erez",
"Tom",
""
],
[
"Smart",
"William D.",
""
]
] |
1203.3482 | Vibhav Gogate | Vibhav Gogate, Pedro Domingos | Formula-Based Probabilistic Inference | Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty
in Artificial Intelligence (UAI2010) | null | null | UAI-P-2010-PG-210-219 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Computing the probability of a formula given the probabilities or weights
associated with other formulas is a natural extension of logical inference to
the probabilistic setting. Surprisingly, this problem has received little
attention in the literature to date, particularly considering that it includes
many standard inference problems as special cases. In this paper, we propose
two algorithms for this problem: formula decomposition and conditioning, which
is an exact method, and formula importance sampling, which is an approximate
method. The latter is, to our knowledge, the first application of model
counting to approximate probabilistic inference. Unlike conventional
variable-based algorithms, our algorithms work in the dual realm of logical
formulas. Theoretically, we show that our algorithms can greatly improve
efficiency by exploiting the structural information in the formulas.
Empirically, we show that they are indeed quite powerful, often achieving
substantial performance gains over state-of-the-art schemes.
| [
{
"version": "v1",
"created": "Thu, 15 Mar 2012 11:17:56 GMT"
}
] | 1,332,115,200,000 | [
[
"Gogate",
"Vibhav",
""
],
[
"Domingos",
"Pedro",
""
]
] |
1203.3490 | Akshat Kumar | Akshat Kumar, Shlomo Zilberstein | Anytime Planning for Decentralized POMDPs using Expectation Maximization | Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty
in Artificial Intelligence (UAI2010) | null | null | UAI-P-2010-PG-294-301 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Decentralized POMDPs provide an expressive framework for multi-agent
sequential decision making. While fnite-horizon DECPOMDPs have enjoyed
signifcant success, progress remains slow for the infnite-horizon case mainly
due to the inherent complexity of optimizing stochastic controllers
representing agent policies. We present a promising new class of algorithms for
the infnite-horizon case, which recasts the optimization problem as inference
in a mixture of DBNs. An attractive feature of this approach is the
straightforward adoption of existing inference techniques in DBNs for solving
DEC-POMDPs and supporting richer representations such as factored or continuous
states and actions. We also derive the Expectation Maximization (EM) algorithm
to optimize the joint policy represented as DBNs. Experiments on benchmark
domains show that EM compares favorably against the state-of-the-art solvers.
| [
{
"version": "v1",
"created": "Thu, 15 Mar 2012 11:17:56 GMT"
}
] | 1,332,115,200,000 | [
[
"Kumar",
"Akshat",
""
],
[
"Zilberstein",
"Shlomo",
""
]
] |
1203.3493 | Yijing Li | Yijing Li, Prakash P. Shenoy | Solving Hybrid Influence Diagrams with Deterministic Variables | Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty
in Artificial Intelligence (UAI2010) | null | null | UAI-P-2010-PG-322-331 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We describe a framework and an algorithm for solving hybrid influence
diagrams with discrete, continuous, and deterministic chance variables, and
discrete and continuous decision variables. A continuous chance variable in an
influence diagram is said to be deterministic if its conditional distributions
have zero variances. The solution algorithm is an extension of Shenoy's fusion
algorithm for discrete influence diagrams. We describe an extended
Shenoy-Shafer architecture for propagation of discrete, continuous, and utility
potentials in hybrid influence diagrams that include deterministic chance
variables. The algorithm and framework are illustrated by solving two small
examples.
| [
{
"version": "v1",
"created": "Thu, 15 Mar 2012 11:17:56 GMT"
}
] | 1,332,115,200,000 | [
[
"Li",
"Yijing",
""
],
[
"Shenoy",
"Prakash P.",
""
]
] |
1203.3499 | Mathias Niepert | Mathias Niepert | A Delayed Column Generation Strategy for Exact k-Bounded MAP Inference
in Markov Logic Networks | Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty
in Artificial Intelligence (UAI2010) | null | null | UAI-P-2010-PG-384-391 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The paper introduces k-bounded MAP inference, a parameterization of MAP
inference in Markov logic networks. k-Bounded MAP states are MAP states with at
most k active ground atoms of hidden (non-evidence) predicates. We present a
novel delayed column generation algorithm and provide empirical evidence that
the algorithm efficiently computes k-bounded MAP states for meaningful
real-world graph matching problems. The underlying idea is that, instead of
solving one large optimization problem, it is often more efficient to tackle
several small ones.
| [
{
"version": "v1",
"created": "Thu, 15 Mar 2012 11:17:56 GMT"
}
] | 1,332,115,200,000 | [
[
"Niepert",
"Mathias",
""
]
] |
1203.3500 | Farheen Omar | Farheen Omar, Mathieu Sinn, Jakub Truszkowski, Pascal Poupart, James
Tung, Allen Caine | Comparative Analysis of Probabilistic Models for Activity Recognition
with an Instrumented Walker | Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty
in Artificial Intelligence (UAI2010) | null | null | UAI-P-2010-PG-392-400 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Rollating walkers are popular mobility aids used by older adults to improve
balance control. There is a need to automatically recognize the activities
performed by walker users to better understand activity patterns, mobility
issues and the context in which falls are more likely to happen. We design and
compare several techniques to recognize walker related activities. A
comprehensive evaluation with control subjects and walker users from a
retirement community is presented.
| [
{
"version": "v1",
"created": "Thu, 15 Mar 2012 11:17:56 GMT"
}
] | 1,332,115,200,000 | [
[
"Omar",
"Farheen",
""
],
[
"Sinn",
"Mathieu",
""
],
[
"Truszkowski",
"Jakub",
""
],
[
"Poupart",
"Pascal",
""
],
[
"Tung",
"James",
""
],
[
"Caine",
"Allen",
""
]
] |
1203.3508 | Guilin Qi | Guilin Qi, Jianfeng Du, Weiru Liu, David A. Bell | Merging Knowledge Bases in Possibilistic Logic by Lexicographic
Aggregation | Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty
in Artificial Intelligence (UAI2010) | null | null | UAI-P-2010-PG-458-465 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Belief merging is an important but difficult problem in Artificial
Intelligence, especially when sources of information are pervaded with
uncertainty. Many merging operators have been proposed to deal with this
problem in possibilistic logic, a weighted logic which is powerful for handling
inconsistency and deal- ing with uncertainty. They often result in a
possibilistic knowledge base which is a set of weighted formulas. Although
possibilistic logic is inconsistency tolerant, it suers from the well-known
"drowning effect". Therefore, we may still want to obtain a consistent possi-
bilistic knowledge base as the result of merg- ing. In such a case, we argue
that it is not always necessary to keep weighted informa- tion after merging.
In this paper, we define a merging operator that maps a set of pos- sibilistic
knowledge bases and a formula rep- resenting the integrity constraints to a
clas- sical knowledge base by using lexicographic ordering. We show that it
satisfies nine pos- tulates that generalize basic postulates for propositional
merging given in [11]. These postulates capture the principle of minimal change
in some sense. We then provide an algorithm for generating the resulting knowl-
edge base of our merging operator. Finally, we discuss the compatibility of our
merging operator with propositional merging and es- tablish the advantage of
our merging opera- tor over existing semantic merging operators in the
propositional case.
| [
{
"version": "v1",
"created": "Thu, 15 Mar 2012 11:17:56 GMT"
}
] | 1,332,115,200,000 | [
[
"Qi",
"Guilin",
""
],
[
"Du",
"Jianfeng",
""
],
[
"Liu",
"Weiru",
""
],
[
"Bell",
"David A.",
""
]
] |
1203.3509 | Erik Quaeghebeur | Erik Quaeghebeur | Characterizing the Set of Coherent Lower Previsions with a Finite Number
of Constraints or Vertices | Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty
in Artificial Intelligence (UAI2010) | null | null | UAI-P-2010-PG-466-473 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The standard coherence criterion for lower previsions is expressed using an
infinite number of linear constraints. For lower previsions that are
essentially defined on some finite set of gambles on a finite possibility
space, we present a reformulation of this criterion that only uses a finite
number of constraints. Any such lower prevision is coherent if it lies within
the convex polytope defined by these constraints. The vertices of this polytope
are the extreme coherent lower previsions for the given set of gambles. Our
reformulation makes it possible to compute them. We show how this is done and
illustrate the procedure and its results.
| [
{
"version": "v1",
"created": "Thu, 15 Mar 2012 11:17:56 GMT"
}
] | 1,332,115,200,000 | [
[
"Quaeghebeur",
"Erik",
""
]
] |
1203.3513 | Ross D. Shachter | Ross D. Shachter, Debarun Bhattacharjya | Dynamic programming in in uence diagrams with decision circuits | Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty
in Artificial Intelligence (UAI2010) | null | null | UAI-P-2010-PG-509-516 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Decision circuits perform efficient evaluation of influence diagrams,
building on the ad- vances in arithmetic circuits for belief net- work
inference [Darwiche, 2003; Bhattachar- jya and Shachter, 2007]. We show how
even more compact decision circuits can be con- structed for dynamic
programming in influ- ence diagrams with separable value functions and
conditionally independent subproblems. Once a decision circuit has been
constructed based on the diagram's "global" graphical structure, it can be
compiled to exploit "lo- cal" structure for efficient evaluation and sen-
sitivity analysis.
| [
{
"version": "v1",
"created": "Thu, 15 Mar 2012 11:17:56 GMT"
}
] | 1,332,115,200,000 | [
[
"Shachter",
"Ross D.",
""
],
[
"Bhattacharjya",
"Debarun",
""
]
] |
1203.3525 | Mark Voortman | Mark Voortman, Denver Dash, Marek J. Druzdzel | Learning Why Things Change: The Difference-Based Causality Learner | Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty
in Artificial Intelligence (UAI2010) | null | null | UAI-P-2010-PG-641-650 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we present the Difference- Based Causality Learner (DBCL), an
algorithm for learning a class of discrete-time dynamic models that represents
all causation across time by means of difference equations driving change in a
system. We motivate this representation with real-world mechanical systems and
prove DBCL's correctness for learning structure from time series data, an
endeavour that is complicated by the existence of latent derivatives that have
to be detected. We also prove that, under common assumptions for causal
discovery, DBCL will identify the presence or absence of feedback loops, making
the model more useful for predicting the effects of manipulating variables when
the system is in equilibrium. We argue analytically and show empirically the
advantages of DBCL over vector autoregression (VAR) and Granger causality
models as well as modified forms of Bayesian and constraintbased structure
discovery algorithms. Finally, we show that our algorithm can discover causal
directions of alpha rhythms in human brains from EEG data.
| [
{
"version": "v1",
"created": "Thu, 15 Mar 2012 11:17:56 GMT"
}
] | 1,332,115,200,000 | [
[
"Voortman",
"Mark",
""
],
[
"Dash",
"Denver",
""
],
[
"Druzdzel",
"Marek J.",
""
]
] |
1203.3528 | Feng Wu | Feng Wu, Shlomo Zilberstein, Xiaoping Chen | Rollout Sampling Policy Iteration for Decentralized POMDPs | Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty
in Artificial Intelligence (UAI2010) | null | null | UAI-P-2010-PG-666-673 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present decentralized rollout sampling policy iteration (DecRSPI) - a new
algorithm for multi-agent decision problems formalized as DEC-POMDPs. DecRSPI
is designed to improve scalability and tackle problems that lack an explicit
model. The algorithm uses Monte- Carlo methods to generate a sample of
reachable belief states. Then it computes a joint policy for each belief state
based on the rollout estimations. A new policy representation allows us to
represent solutions compactly. The key benefits of the algorithm are its linear
time complexity over the number of agents, its bounded memory usage and good
solution quality. It can solve larger problems that are intractable for
existing planning algorithms. Experimental results confirm the effectiveness
and scalability of the approach.
| [
{
"version": "v1",
"created": "Thu, 15 Mar 2012 11:17:56 GMT"
}
] | 1,332,115,200,000 | [
[
"Wu",
"Feng",
""
],
[
"Zilberstein",
"Shlomo",
""
],
[
"Chen",
"Xiaoping",
""
]
] |
1203.3531 | Changhe Yuan | Changhe Yuan, Xiaojian Wu, Eric A. Hansen | Solving Multistage Influence Diagrams using Branch-and-Bound Search | Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty
in Artificial Intelligence (UAI2010) | null | null | UAI-P-2010-PG-691-700 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A branch-and-bound approach to solving influ- ence diagrams has been
previously proposed in the literature, but appears to have never been
implemented and evaluated - apparently due to the difficulties of computing
effective bounds for the branch-and-bound search. In this paper, we describe
how to efficiently compute effective bounds, and we develop a practical
implementa- tion of depth-first branch-and-bound search for influence diagram
evaluation that outperforms existing methods for solving influence diagrams
with multiple stages.
| [
{
"version": "v1",
"created": "Thu, 15 Mar 2012 11:17:56 GMT"
}
] | 1,332,115,200,000 | [
[
"Yuan",
"Changhe",
""
],
[
"Wu",
"Xiaojian",
""
],
[
"Hansen",
"Eric A.",
""
]
] |
1203.3538 | Emma Brunskill | Emma Brunskill, Stuart Russell | RAPID: A Reachable Anytime Planner for Imprecisely-sensed Domains | Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty
in Artificial Intelligence (UAI2010) | null | null | UAI-P-2010-PG-83-92 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite the intractability of generic optimal partially observable Markov
decision process planning, there exist important problems that have highly
structured models. Previous researchers have used this insight to construct
more efficient algorithms for factored domains, and for domains with
topological structure in the flat state dynamics model. In our work, motivated
by findings from the education community relevant to automated tutoring, we
consider problems that exhibit a form of topological structure in the factored
dynamics model. Our Reachable Anytime Planner for Imprecisely-sensed Domains
(RAPID) leverages this structure to efficiently compute a good initial envelope
of reachable states under the optimal MDP policy in time linear in the number
of state variables. RAPID performs partially-observable planning over the
limited envelope of states, and slowly expands the state space considered as
time allows. RAPID performs well on a large tutoring-inspired problem
simulation with 122 state variables, corresponding to a flat state space of
over 10^30 states.
| [
{
"version": "v1",
"created": "Thu, 15 Mar 2012 11:25:52 GMT"
}
] | 1,332,115,200,000 | [
[
"Brunskill",
"Emma",
""
],
[
"Russell",
"Stuart",
""
]
] |
1203.4011 | Raghuram Ramanujan | Raghuram Ramanujan, Ashish Sabharwal, Bart Selman | Understanding Sampling Style Adversarial Search Methods | Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty
in Artificial Intelligence (UAI2010) | null | null | UAI-P-2010-PG-474-483 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | UCT has recently emerged as an exciting new adversarial reasoning technique
based on cleverly balancing exploration and exploitation in a Monte-Carlo
sampling setting. It has been particularly successful in the game of Go but the
reasons for its success are not well understood and attempts to replicate its
success in other domains such as Chess have failed. We provide an in-depth
analysis of the potential of UCT in domain-independent settings, in cases where
heuristic values are available, and the effect of enhancing random playouts to
more informed playouts between two weak minimax players. To provide further
insights, we develop synthetic game tree instances and discuss interesting
properties of UCT, both empirically and analytically.
| [
{
"version": "v1",
"created": "Thu, 15 Mar 2012 11:17:56 GMT"
}
] | 1,332,201,600,000 | [
[
"Ramanujan",
"Raghuram",
""
],
[
"Sabharwal",
"Ashish",
""
],
[
"Selman",
"Bart",
""
]
] |
1203.4287 | Muhammad Islam | Muhammad Asiful Islam, C. R. Ramakrishnan, I. V. Ramakrishnan | Parameter Learning in PRISM Programs with Continuous Random Variables | 7 pages. Main contribution: Learning algorithm. Inference appears in
http://arxiv.org/abs/1112.2681 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Probabilistic Logic Programming (PLP), exemplified by Sato and Kameya's
PRISM, Poole's ICL, De Raedt et al's ProbLog and Vennekens et al's LPAD,
combines statistical and logical knowledge representation and inference.
Inference in these languages is based on enumerative construction of proofs
over logic programs. Consequently, these languages permit very limited use of
random variables with continuous distributions. In this paper, we extend PRISM
with Gaussian random variables and linear equality constraints, and consider
the problem of parameter learning in the extended language. Many statistical
models such as finite mixture models and Kalman filter can be encoded in
extended PRISM. Our EM-based learning algorithm uses a symbolic inference
procedure that represents sets of derivations without enumeration. This permits
us to learn the distribution parameters of extended PRISM programs with
discrete as well as Gaussian variables. The learning algorithm naturally
generalizes the ones used for PRISM and Hybrid Bayesian Networks.
| [
{
"version": "v1",
"created": "Mon, 19 Mar 2012 23:37:07 GMT"
}
] | 1,332,288,000,000 | [
[
"Islam",
"Muhammad Asiful",
""
],
[
"Ramakrishnan",
"C. R.",
""
],
[
"Ramakrishnan",
"I. V.",
""
]
] |
1203.5452 | Nesrine Yahia Ben | Nesrine Ben Yahia, Narj\`es Bellamine and Henda Ben Ghezala | Modeling of Mixed Decision Making Process | Keywords-collaborative knowledge management; mixed decision making;
dynamicity of actors; UML-G | In Proceedings of IEEE International Conference on Information
Technology and e-Services 2012, pp. 555-559 ISBN: 978-9938-9511-1-0 | null | null | cs.AI | http://creativecommons.org/licenses/by/3.0/ | Decision making whenever and wherever it is happened is key to organizations
success. In order to make correct decision, individuals, teams and
organizations need both knowledge management (to manage content) and
collaboration (to manage group processes) to make that more effective and
efficient. In this paper, we explain the knowledge management and collaboration
convergence. Then, we propose a formal description of mixed and multimodal
decision making (MDM) process where decision may be made by three possible
modes: individual, collective or hybrid. Finally, we explicit the MDM process
based on UML-G profile.
| [
{
"version": "v1",
"created": "Sat, 24 Mar 2012 22:18:36 GMT"
}
] | 1,332,806,400,000 | [
[
"Yahia",
"Nesrine Ben",
""
],
[
"Bellamine",
"Narjès",
""
],
[
"Ghezala",
"Henda Ben",
""
]
] |
1203.5532 | Bruno Scherrer | Bruno Scherrer (INRIA Lorraine - LORIA) | On the Use of Non-Stationary Policies for Infinite-Horizon Discounted
Markov Decision Processes | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider infinite-horizon $\gamma$-discounted Markov Decision Processes,
for which it is known that there exists a stationary optimal policy. We
consider the algorithm Value Iteration and the sequence of policies
$\pi_1,...,\pi_k$ it implicitely generates until some iteration $k$. We provide
performance bounds for non-stationary policies involving the last $m$ generated
policies that reduce the state-of-the-art bound for the last stationary policy
$\pi_k$ by a factor $\frac{1-\gamma}{1-\gamma^m}$. In particular, the use of
non-stationary policies allows to reduce the usual asymptotic performance
bounds of Value Iteration with errors bounded by $\epsilon$ at each iteration
from $\frac{\gamma}{(1-\gamma)^2}\epsilon$ to
$\frac{\gamma}{1-\gamma}\epsilon$, which is significant in the usual situation
when $\gamma$ is close to 1. Given Bellman operators that can only be computed
with some error $\epsilon$, a surprising consequence of this result is that the
problem of "computing an approximately optimal non-stationary policy" is much
simpler than that of "computing an approximately optimal stationary policy",
and even slightly simpler than that of "approximately computing the value of
some fixed policy", since this last problem only has a guarantee of
$\frac{1}{1-\gamma}\epsilon$.
| [
{
"version": "v1",
"created": "Sun, 25 Mar 2012 19:44:41 GMT"
},
{
"version": "v2",
"created": "Fri, 30 Mar 2012 18:18:05 GMT"
}
] | 1,333,324,800,000 | [
[
"Scherrer",
"Bruno",
"",
"INRIA Lorraine - LORIA"
]
] |
1203.6716 | Gopalakrishnan Tr Nair | Dr T.R. Gopalakrishnan Nair, Meenakshi Malhotra | Creating Intelligent Linking for Information Threading in Knowledge
Networks | 5 Pages, 6 Figures, 2 Tables, India Conference (INDICON), 2011 | India Conference (INDICON), 2011 | 10.1109/INDCON.2011.6139335 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Informledge System (ILS) is a knowledge network with autonomous nodes and
intelligent links that integrate and structure the pieces of knowledge. In this
paper, we aim to put forward the link dynamics involved in intelligent
processing of information in ILS. There has been advancement in knowledge
management field which involve managing information in databases from a single
domain. ILS works with information from multiple domains stored in distributed
way in the autonomous nodes termed as Knowledge Network Node (KNN). Along with
the concept under consideration, KNNs store the processed information linking
concepts and processors leading to the appropriate processing of information.
| [
{
"version": "v1",
"created": "Fri, 30 Mar 2012 05:18:06 GMT"
}
] | 1,333,324,800,000 | [
[
"Nair",
"Dr T. R. Gopalakrishnan",
""
],
[
"Malhotra",
"Meenakshi",
""
]
] |
1204.0181 | Youssef Bassil | Youssef Bassil | Expert PC Troubleshooter With Fuzzy-Logic And Self-Learning Support | LACSC - Lebanese Association for Computational Sciences,
http://www.lacsc.org/; International Journal of Artificial Intelligence &
Applications (IJAIA), Vol.3, No.2, March 2012 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Expert systems use human knowledge often stored as rules within the computer
to solve problems that generally would entail human intelligence. Today, with
information systems turning out to be more pervasive and with the myriad
advances in information technologies, automating computer fault diagnosis is
becoming so fundamental that soon every enterprise has to endorse it. This
paper proposes an expert system called Expert PC Troubleshooter for diagnosing
computer problems. The system is composed of a user interface, a rule-base, an
inference engine, and an expert interface. Additionally, the system features a
fuzzy-logic module to troubleshoot POST beep errors, and an intelligent agent
that assists in the knowledge acquisition process. The proposed system is meant
to automate the maintenance, repair, and operations (MRO) process, and free-up
human technicians from manually performing routine, laborious, and
timeconsuming maintenance tasks. As future work, the proposed system is to be
parallelized so as to boost its performance and speed-up its various
operations.
| [
{
"version": "v1",
"created": "Sun, 1 Apr 2012 09:08:21 GMT"
}
] | 1,333,411,200,000 | [
[
"Bassil",
"Youssef",
""
]
] |
1204.0731 | Olivier Bailleux | Olivier Bailleux | Unit contradiction versus unit propagation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Some aspects of the result of applying unit resolution on a CNF formula can
be formalized as functions with domain a set of partial truth assignments. We
are interested in two ways for computing such functions, depending on whether
the result is the production of the empty clause or the assignment of a
variable with a given truth value. We show that these two models can compute
the same functions with formulae of polynomially related sizes, and we explain
how this result is related to the CNF encoding of Boolean constraints.
| [
{
"version": "v1",
"created": "Tue, 3 Apr 2012 16:44:47 GMT"
}
] | 1,333,497,600,000 | [
[
"Bailleux",
"Olivier",
""
]
] |
1204.1576 | Sanjeev Jha | Sanjeev Kumar Jha | Development of knowledge Base Expert System for Natural treatment of
Diabetes disease | null | International Journal of Advanced Computer Science and
Applications(IJACSA)Volume 3 Issue 3 March 2012 Published | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The development of expert system for treatment of Diabetes disease by using
natural methods is new information technology derived from Artificial
Intelligent research using ESTA (Expert System Text Animation) System. The
proposed expert system contains knowledge about various methods of natural
treatment methods (Massage, Herbal/Proper Nutrition, Acupuncture, Gems) for
Diabetes diseases of Human Beings. The system is developed in the ESTA (Expert
System shell for Text Animation) which is Visual Prolog 7.3 Application. The
knowledge for the said system will be acquired from domain experts, texts and
other related sources.
| [
{
"version": "v1",
"created": "Fri, 6 Apr 2012 22:35:15 GMT"
}
] | 1,334,016,000,000 | [
[
"Jha",
"Sanjeev Kumar",
""
]
] |
1204.1637 | Mohamed Ali Mahjoub | Nabil ghanmy, Mohamed Ali Mahjoub, Najoua Essoukri Ben Amara | Characterization of Dynamic Bayesian Network | 9 pages, (IJACSA) International Journal of Advanced Computer Science
and Applications, Vol. 2, No. 7, 2011 | null | null | 2156-5570 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this report, we will be interested at Dynamic Bayesian Network (DBNs) as a
model that tries to incorporate temporal dimension with uncertainty. We start
with basics of DBN where we especially focus in Inference and Learning concepts
and algorithms. Then we will present different levels and methods of creating
DBNs as well as approaches of incorporating temporal dimension in static
Bayesian network.
| [
{
"version": "v1",
"created": "Sat, 7 Apr 2012 13:55:29 GMT"
}
] | 1,334,188,800,000 | [
[
"ghanmy",
"Nabil",
""
],
[
"Mahjoub",
"Mohamed Ali",
""
],
[
"Amara",
"Najoua Essoukri Ben",
""
]
] |
1204.1653 | Ali Elouafiq | Ali Elouafiq | Machine Cognition Models: EPAM and GPS | EPAM, General Problem solver | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Through history, the human being tried to relay its daily tasks to other
creatures, which was the main reason behind the rise of civilizations. It
started with deploying animals to automate tasks in the field of
agriculture(bulls), transportation (e.g. horses and donkeys), and even
communication (pigeons). Millenniums after, come the Golden age with
"Al-jazari" and other Muslim inventors, which were the pioneers of automation,
this has given birth to industrial revolution in Europe, centuries after. At
the end of the nineteenth century, a new era was to begin, the computational
era, the most advanced technological and scientific development that is driving
the mankind and the reason behind all the evolutions of science; such as
medicine, communication, education, and physics. At this edge of technology
engineers and scientists are trying to model a machine that behaves the same as
they do, which pushed us to think about designing and implementing "Things
that-Thinks", then artificial intelligence was. In this work we will cover each
of the major discoveries and studies in the field of machine cognition, which
are the "Elementary Perceiver and Memorizer"(EPAM) and "The General Problem
Solver"(GPS). The First one focus mainly on implementing the human-verbal
learning behavior, while the second one tries to model an architecture that is
able to solve problems generally (e.g. theorem proving, chess playing, and
arithmetic). We will cover the major goals and the main ideas of each model, as
well as comparing their strengths and weaknesses, and finally giving their
fields of applications. And Finally, we will suggest a real life implementation
of a cognitive machine.
| [
{
"version": "v1",
"created": "Sat, 7 Apr 2012 16:34:20 GMT"
}
] | 1,334,016,000,000 | [
[
"Elouafiq",
"Ali",
""
]
] |
1204.1851 | Alexander Artikis | Anastasios Skarlatidis, Alexander Artikis, Jason Filippou and Georgios
Paliouras | A Probabilistic Logic Programming Event Calculus | Accepted for publication in the Theory and Practice of Logic
Programming (TPLP) journal | Theory and Practice of Logic Programming 15 (2015) 213-245 | 10.1017/S1471068413000690 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a system for recognising human activity given a symbolic
representation of video content. The input of our system is a set of
time-stamped short-term activities (STA) detected on video frames. The output
is a set of recognised long-term activities (LTA), which are pre-defined
temporal combinations of STA. The constraints on the STA that, if satisfied,
lead to the recognition of a LTA, have been expressed using a dialect of the
Event Calculus. In order to handle the uncertainty that naturally occurs in
human activity recognition, we adapted this dialect to a state-of-the-art
probabilistic logic programming framework. We present a detailed evaluation and
comparison of the crisp and probabilistic approaches through experimentation on
a benchmark dataset of human surveillance videos.
| [
{
"version": "v1",
"created": "Mon, 9 Apr 2012 10:23:38 GMT"
},
{
"version": "v2",
"created": "Mon, 29 Apr 2013 16:15:27 GMT"
}
] | 1,582,070,400,000 | [
[
"Skarlatidis",
"Anastasios",
""
],
[
"Artikis",
"Alexander",
""
],
[
"Filippou",
"Jason",
""
],
[
"Paliouras",
"Georgios",
""
]
] |
1204.2018 | Igor Subbotin | Igor Ya. Subbotin and Michael Gr. Voskoglou | Applications of fuzzy logic to Case-Based Reasoning | null | International Journal of Applications of Fuzzy Sets (ISSN
2241-1240) Vol. 1 ( 2011), 7-18 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The article discusses some applications of fuzzy logic ideas to formalizing
of the Case-Based Reasoning (CBR) process and to measuring the effectiveness of
CBR systems
| [
{
"version": "v1",
"created": "Tue, 10 Apr 2012 00:59:28 GMT"
}
] | 1,334,102,400,000 | [
[
"Subbotin",
"Igor Ya.",
""
],
[
"Voskoglou",
"Michael Gr.",
""
]
] |
1204.3255 | Manfred Jaeger | Manfred Jaeger | Lower Complexity Bounds for Lifted Inference | To appear in Theory and Practice of Logic Programming (TPLP) | Theory and Practice of Logic Programming 15 (2015) 246-263 | 10.1017/S1471068413000707 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the big challenges in the development of probabilistic relational (or
probabilistic logical) modeling and learning frameworks is the design of
inference techniques that operate on the level of the abstract model
representation language, rather than on the level of ground, propositional
instances of the model. Numerous approaches for such "lifted inference"
techniques have been proposed. While it has been demonstrated that these
techniques will lead to significantly more efficient inference on some specific
models, there are only very recent and still quite restricted results that show
the feasibility of lifted inference on certain syntactically defined classes of
models. Lower complexity bounds that imply some limitations for the feasibility
of lifted inference on more expressive model classes were established early on
in (Jaeger 2000). However, it is not immediate that these results also apply to
the type of modeling languages that currently receive the most attention, i.e.,
weighted, quantifier-free formulas. In this paper we extend these earlier
results, and show that under the assumption that NETIME =/= ETIME, there is no
polynomial lifted inference algorithm for knowledge bases of weighted,
quantifier- and function-free formulas. Further strengthening earlier results,
this is also shown to hold for approximate inference, and for knowledge bases
not containing the equality predicate.
| [
{
"version": "v1",
"created": "Sun, 15 Apr 2012 10:59:29 GMT"
},
{
"version": "v2",
"created": "Thu, 2 May 2013 15:27:06 GMT"
}
] | 1,582,070,400,000 | [
[
"Jaeger",
"Manfred",
""
]
] |
1204.3844 | Abdelmalik Moujahid | Blanca Cases, Alicia D'Anjou, Abdelmalik Moujahid | On how percolation threshold affects PSO performance | null | LNCS, 2012, Volume 7208/2012, 509-520 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Statistical evidence of the influence of neighborhood topology on the
performance of particle swarm optimization (PSO) algorithms has been shown in
many works. However, little has been done about the implications could have the
percolation threshold in determining the topology of this neighborhood. This
work addresses this problem for individuals that, like robots, are able to
sense in a limited neighborhood around them. Based on the concept of
percolation threshold, and more precisely, the disk percolation model in 2D, we
show that better results are obtained for low values of radius, when
individuals occasionally ask others their best visited positions, with the
consequent decrease of computational complexity. On the other hand, since
percolation threshold is a universal measure, it could have a great interest to
compare the performance of different hybrid PSO algorithms.
| [
{
"version": "v1",
"created": "Tue, 17 Apr 2012 17:00:58 GMT"
}
] | 1,334,707,200,000 | [
[
"Cases",
"Blanca",
""
],
[
"D'Anjou",
"Alicia",
""
],
[
"Moujahid",
"Abdelmalik",
""
]
] |
1204.4051 | Martin Josef Geiger | Thibaut Barth\'elemy, Martin Josef Geiger, Marc Sevaux | Solution Representations and Local Search for the bi-objective Inventory
Routing Problem | Proceedings of EU/ME 2012, Workshop on Metaheuristics for Global
Challenges, May 10-11, 2012, Copenhagen, Denmark | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The solution of the biobjective IRP is rather challenging, even for
metaheuristics. We are still lacking a profound understanding of appropriate
solution representations and effective neighborhood structures. Clearly, both
the delivery volumes and the routing aspects of the alternatives need to be
reflected in an encoding, and must be modified when searching by means of local
search. Our work contributes to the better understanding of such solution
representations. On the basis of an experimental investigation, the advantages
and drawbacks of two encodings are studied and compared.
| [
{
"version": "v1",
"created": "Wed, 18 Apr 2012 11:32:07 GMT"
}
] | 1,334,793,600,000 | [
[
"Barthélemy",
"Thibaut",
""
],
[
"Geiger",
"Martin Josef",
""
],
[
"Sevaux",
"Marc",
""
]
] |
1204.4541 | Patrick Taillandier | Patrick Taillandier (UMMISCO), Julien Gaffuri (COGIT) | Automatic Sampling of Geographic objects | null | GIScience, Zurich : Switzerland (2010) | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Today, one's disposes of large datasets composed of thousands of geographic
objects. However, for many processes, which require the appraisal of an expert
or much computational time, only a small part of these objects can be taken
into account. In this context, robust sampling methods become necessary. In
this paper, we propose a sampling method based on clustering techniques. Our
method consists in dividing the objects in clusters, then in selecting in each
cluster, the most representative objects. A case-study in the context of a
process dedicated to knowledge revision for geographic data generalisation is
presented. This case-study shows that our method allows to select relevant
samples of objects.
| [
{
"version": "v1",
"created": "Fri, 20 Apr 2012 06:35:41 GMT"
}
] | 1,335,139,200,000 | [
[
"Taillandier",
"Patrick",
"",
"UMMISCO"
],
[
"Gaffuri",
"Julien",
"",
"COGIT"
]
] |
1204.4989 | Patrick Taillandier | Patrick Taillandier (COGIT, UMMISCO), C\'ecile Duch\^ene (COGIT),
Alexis Drogoul (UMMISCO, MSI) | Using Belief Theory to Diagnose Control Knowledge Quality. Application
to cartographic generalisation | Best paper award, International Conference on Computing and
Communication Technologies (IEEE-RIVF), Danang : Viet Nam (2009) | null | 10.1109/RIVF.2009.5174663 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Both humans and artificial systems frequently use trial and error methods to
problem solving. In order to be effective, this type of strategy implies having
high quality control knowledge to guide the quest for the optimal solution.
Unfortunately, this control knowledge is rarely perfect. Moreover, in
artificial systems-as in humans-self-evaluation of one's own knowledge is often
difficult. Yet, this self-evaluation can be very useful to manage knowledge and
to determine when to revise it. The objective of our work is to propose an
automated approach to evaluate the quality of control knowledge in artificial
systems based on a specific trial and error strategy, namely the informed tree
search strategy. Our revision approach consists in analysing the system's
execution logs, and in using the belief theory to evaluate the global quality
of the knowledge. We present a real-world industrial application in the form of
an experiment using this approach in the domain of cartographic generalisation.
Thus far, the results of using our approach have been encouraging.
| [
{
"version": "v1",
"created": "Mon, 23 Apr 2012 08:01:48 GMT"
}
] | 1,335,225,600,000 | [
[
"Taillandier",
"Patrick",
"",
"COGIT, UMMISCO"
],
[
"Duchêne",
"Cécile",
"",
"COGIT"
],
[
"Drogoul",
"Alexis",
"",
"UMMISCO, MSI"
]
] |
1204.6415 | Michael Gr. Voskoglou Prof. Dr. | Michael Gr. Voskoglou | A Fuzzy Model for Analogical Problem Solving | 10 pages, 1 Table | International Journal of Fuzzy Logic Systems Vol. 2, No. 1, pp.
1-10, February 2012 | 10.5121/ijfls.2012.2101 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we develop a fuzzy model for the description of the process of
Analogical Reasoning by representing its main steps as fuzzy subsets of a set
of linguistic labels characterizing the individuals' performance in each step
and we use the Shannon- Wiener diversity index as a measure of the individuals'
abilities in analogical problem solving. This model is compared with a
stochastic model presented in author's earlier papers by introducing a finite
Markov chain on the steps of the process of Analogical Reasoning. A classroom
experiment is also presented to illustrate the use of our results in practice.
| [
{
"version": "v1",
"created": "Sat, 28 Apr 2012 16:16:46 GMT"
}
] | 1,335,830,400,000 | [
[
"Voskoglou",
"Michael Gr.",
""
]
] |
1205.1645 | Fran\c{c}ois Scharffe | Julien Plu and Fran\c{c}ois Scharffe | Publishing and linking transport data on the Web | Presented at the First International Workshop On Open Data, WOD-2012
(http://arxiv.org/abs/1204.3726) | null | null | WOD/2012/NANTES/13 | cs.AI | http://creativecommons.org/licenses/by-nc-sa/3.0/ | Without Linked Data, transport data is limited to applications exclusively
around transport. In this paper, we present a workflow for publishing and
linking transport data on the Web. So we will be able to develop transport
applications and to add other features which will be created from other
datasets. This will be possible because transport data will be linked to these
datasets. We apply this workflow to two datasets: NEPTUNE, a French standard
describing a transport line, and Passim, a directory containing relevant
information on transport services, in every French city.
| [
{
"version": "v1",
"created": "Tue, 8 May 2012 09:50:35 GMT"
}
] | 1,336,521,600,000 | [
[
"Plu",
"Julien",
""
],
[
"Scharffe",
"François",
""
]
] |
1205.2541 | Changzhong Wang | Changzhong Wang, Baiqing Sun, Qinhua Hu | An improved approach to attribute reduction with covering rough sets | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Attribute reduction is viewed as an important preprocessing step for pattern
recognition and data mining. Most of researches are focused on attribute
reduction by using rough sets. Recently, Tsang et al. discussed attribute
reduction with covering rough sets in the paper [E. C.C. Tsang, D. Chen, Daniel
S. Yeung, Approximations and reducts with covering generalized rough sets,
Computers and Mathematics with Applications 56 (2008) 279-289], where an
approach based on discernibility matrix was presented to compute all attribute
reducts. In this paper, we provide an improved approach by constructing simpler
discernibility matrix with covering rough sets, and then proceed to improve
some characterizations of attribute reduction provided by Tsang et al. It is
proved that the improved discernible matrix is equivalent to the old one, but
the computational complexity of discernible matrix is greatly reduced.
| [
{
"version": "v1",
"created": "Fri, 11 May 2012 14:45:52 GMT"
}
] | 1,336,953,600,000 | [
[
"Wang",
"Changzhong",
""
],
[
"Sun",
"Baiqing",
""
],
[
"Hu",
"Qinhua",
""
]
] |
1205.2596 | Fabio Cozman | Fabio Cozman and Avi Pfeffer | Proceedings of the Twenty-Seventh Conference on Uncertainty in
Artificial Intelligence (2011) | null | null | null | UAI2011 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This is the Proceedings of the Twenty-Seventh Conference on Uncertainty in
Artificial Intelligence, which was held in Barcelona, Spain, July 14 - 17 2011.
| [
{
"version": "v1",
"created": "Fri, 11 May 2012 18:35:50 GMT"
},
{
"version": "v2",
"created": "Thu, 28 Aug 2014 04:30:01 GMT"
}
] | 1,409,270,400,000 | [
[
"Cozman",
"Fabio",
""
],
[
"Pfeffer",
"Avi",
""
]
] |
1205.2597 | Peter Grunwald | Peter Grunwald and Peter Spirtes | Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial
Intelligence (2010) | null | null | null | UAI2010 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This is the Proceedings of the Twenty-Sixth Conference on Uncertainty in
Artificial Intelligence, which was held on Catalina Island, CA, July 8 - 11
2010.
| [
{
"version": "v1",
"created": "Fri, 11 May 2012 18:40:29 GMT"
},
{
"version": "v2",
"created": "Thu, 28 Aug 2014 04:29:00 GMT"
}
] | 1,409,270,400,000 | [
[
"Grunwald",
"Peter",
""
],
[
"Spirtes",
"Peter",
""
]
] |
1205.2601 | Changhe Yuan | Changhe Yuan, Xiaolu Liu, Tsai-Ching Lu, Heejin Lim | Most Relevant Explanation: Properties, Algorithms, and Evaluations | Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty
in Artificial Intelligence (UAI2009) | null | null | UAI-P-2009-PG-631-638 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most Relevant Explanation (MRE) is a method for finding multivariate
explanations for given evidence in Bayesian networks [12]. This paper studies
the theoretical properties of MRE and develops an algorithm for finding
multiple top MRE solutions. Our study shows that MRE relies on an implicit soft
relevance measure in automatically identifying the most relevant target
variables and pruning less relevant variables from an explanation. The soft
measure also enables MRE to capture the intuitive phenomenon of explaining away
encoded in Bayesian networks. Furthermore, our study shows that the solution
space of MRE has a special lattice structure which yields interesting dominance
relations among the solutions. A K-MRE algorithm based on these dominance
relations is developed for generating a set of top solutions that are more
representative. Our empirical results show that MRE methods are promising
approaches for explanation in Bayesian networks.
| [
{
"version": "v1",
"created": "Wed, 9 May 2012 18:47:26 GMT"
}
] | 1,336,953,600,000 | [
[
"Yuan",
"Changhe",
""
],
[
"Liu",
"Xiaolu",
""
],
[
"Lu",
"Tsai-Ching",
""
],
[
"Lim",
"Heejin",
""
]
] |
1205.2613 | Matthias Thimm | Matthias Thimm | Measuring Inconsistency in Probabilistic Knowledge Bases | Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty
in Artificial Intelligence (UAI2009) | null | null | UAI-P-2009-PG-530-537 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper develops an inconsistency measure on conditional probabilistic
knowledge bases. The measure is based on fundamental principles for
inconsistency measures and thus provides a solid theoretical framework for the
treatment of inconsistencies in probabilistic expert systems. We illustrate its
usefulness and immediate application on several examples and present some
formal results. Building on this measure we use the Shapley value-a well-known
solution for coalition games-to define a sophisticated indicator that is not
only able to measure inconsistencies but to reveal the causes of
inconsistencies in the knowledge base. Altogether these tools guide the
knowledge engineer in his aim to restore consistency and therefore enable him
to build a consistent and usable knowledge base that can be employed in
probabilistic expert systems.
| [
{
"version": "v1",
"created": "Wed, 9 May 2012 18:31:58 GMT"
}
] | 1,336,953,600,000 | [
[
"Thimm",
"Matthias",
""
]
] |
1205.2616 | Prithviraj Sen | Prithviraj Sen, Amol Deshpande, Lise Getoor | Bisimulation-based Approximate Lifted Inference | Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty
in Artificial Intelligence (UAI2009) | null | null | UAI-P-2009-PG-496-505 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There has been a great deal of recent interest in methods for performing
lifted inference; however, most of this work assumes that the first-order model
is given as input to the system. Here, we describe lifted inference algorithms
that determine symmetries and automatically lift the probabilistic model to
speedup inference. In particular, we describe approximate lifted inference
techniques that allow the user to trade off inference accuracy for
computational efficiency by using a handful of tunable parameters, while
keeping the error bounded. Our algorithms are closely related to the
graph-theoretic concept of bisimulation. We report experiments on both
synthetic and real data to show that in the presence of symmetries, run-times
for inference can be improved significantly, with approximate lifted inference
providing orders of magnitude speedup over ground inference.
| [
{
"version": "v1",
"created": "Wed, 9 May 2012 18:27:56 GMT"
}
] | 1,336,953,600,000 | [
[
"Sen",
"Prithviraj",
""
],
[
"Deshpande",
"Amol",
""
],
[
"Getoor",
"Lise",
""
]
] |
1205.2619 | Kevin Regan | Kevin Regan, Craig Boutilier | Regret-based Reward Elicitation for Markov Decision Processes | Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty
in Artificial Intelligence (UAI2009) | null | null | UAI-P-2009-PG-444-451 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The specification of aMarkov decision process (MDP) can be difficult. Reward
function specification is especially problematic; in practice, it is often
cognitively complex and time-consuming for users to precisely specify rewards.
This work casts the problem of specifying rewards as one of preference
elicitation and aims to minimize the degree of precision with which a reward
function must be specified while still allowing optimal or near-optimal
policies to be produced. We first discuss how robust policies can be computed
for MDPs given only partial reward information using the minimax regret
criterion. We then demonstrate how regret can be reduced by efficiently
eliciting reward information using bound queries, using regret-reduction as a
means for choosing suitable queries. Empirical results demonstrate that
regret-based reward elicitation offers an effective way to produce near-optimal
policies without resorting to the precise specification of the entire reward
function.
| [
{
"version": "v1",
"created": "Wed, 9 May 2012 18:23:30 GMT"
}
] | 1,336,953,600,000 | [
[
"Regan",
"Kevin",
""
],
[
"Boutilier",
"Craig",
""
]
] |
1205.2621 | Mathias Niepert | Mathias Niepert | Logical Inference Algorithms and Matrix Representations for
Probabilistic Conditional Independence | Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty
in Artificial Intelligence (UAI2009) | null | null | UAI-P-2009-PG-428-435 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Logical inference algorithms for conditional independence (CI) statements
have important applications from testing consistency during knowledge
elicitation to constraintbased structure learning of graphical models. We prove
that the implication problem for CI statements is decidable, given that the
size of the domains of the random variables is known and fixed. We will present
an approximate logical inference algorithm which combines a falsification and a
novel validation algorithm. The validation algorithm represents each set of CI
statements as a sparse 0-1 matrix A and validates instances of the implication
problem by solving specific linear programs with constraint matrix A. We will
show experimentally that the algorithm is both effective and efficient in
validating and falsifying instances of the probabilistic CI implication
problem.
| [
{
"version": "v1",
"created": "Wed, 9 May 2012 17:28:17 GMT"
}
] | 1,336,953,600,000 | [
[
"Niepert",
"Mathias",
""
]
] |
1205.2634 | Samantha Kleinberg | Samantha Kleinberg, Bud Mishra | The Temporal Logic of Causal Structures | Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty
in Artificial Intelligence (UAI2009) | null | null | UAI-P-2009-PG-303-312 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Computational analysis of time-course data with an underlying causal
structure is needed in a variety of domains, including neural spike trains,
stock price movements, and gene expression levels. However, it can be
challenging to determine from just the numerical time course data alone what is
coordinating the visible processes, to separate the underlying prima facie
causes into genuine and spurious causes and to do so with a feasible
computational complexity. For this purpose, we have been developing a novel
algorithm based on a framework that combines notions of causality in philosophy
with algorithmic approaches built on model checking and statistical techniques
for multiple hypotheses testing. The causal relationships are described in
terms of temporal logic formulae, reframing the inference problem in terms of
model checking. The logic used, PCTL, allows description of both the time
between cause and effect and the probability of this relationship being
observed. We show that equipped with these causal formulae with their
associated probabilities we may compute the average impact a cause makes to its
effect and then discover statistically significant causes through the concepts
of multiple hypothesis testing (treating each causal relationship as a
hypothesis), and false discovery control. By exploring a well-chosen family of
potentially all significant hypotheses with reasonably minimal description
length, it is possible to tame the algorithm's computational complexity while
exploring the nearly complete search-space of all prima facie causes. We have
tested these ideas in a number of domains and illustrate them here with two
examples.
| [
{
"version": "v1",
"created": "Wed, 9 May 2012 15:45:06 GMT"
}
] | 1,336,953,600,000 | [
[
"Kleinberg",
"Samantha",
""
],
[
"Mishra",
"Bud",
""
]
] |
1205.2635 | Jacek Kisynski | Jacek Kisynski, David L Poole | Constraint Processing in Lifted Probabilistic Inference | Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty
in Artificial Intelligence (UAI2009) | null | null | UAI-P-2009-PG-293-302 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | First-order probabilistic models combine representational power of
first-order logic with graphical models. There is an ongoing effort to design
lifted inference algorithms for first-order probabilistic models. We analyze
lifted inference from the perspective of constraint processing and, through
this viewpoint, we analyze and compare existing approaches and expose their
advantages and limitations. Our theoretical results show that the wrong choice
of constraint processing method can lead to exponential increase in
computational complexity. Our empirical tests confirm the importance of
constraint processing in lifted inference. This is the first theoretical and
empirical study of constraint processing in lifted inference.
| [
{
"version": "v1",
"created": "Wed, 9 May 2012 15:41:10 GMT"
}
] | 1,336,953,600,000 | [
[
"Kisynski",
"Jacek",
""
],
[
"Poole",
"David L",
""
]
] |
1205.2637 | Kristian Kersting | Kristian Kersting, Babak Ahmadi, Sriraam Natarajan | Counting Belief Propagation | Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty
in Artificial Intelligence (UAI2009) | null | null | UAI-P-2009-PG-277-284 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A major benefit of graphical models is that most knowledge is captured in the
model structure. Many models, however, produce inference problems with a lot of
symmetries not reflected in the graphical structure and hence not exploitable
by efficient inference techniques such as belief propagation (BP). In this
paper, we present a new and simple BP algorithm, called counting BP, that
exploits such additional symmetries. Starting from a given factor graph,
counting BP first constructs a compressed factor graph of clusternodes and
clusterfactors, corresponding to sets of nodes and factors that are
indistinguishable given the evidence. Then it runs a modified BP algorithm on
the compressed graph that is equivalent to running BP on the original factor
graph. Our experiments show that counting BP is applicable to a variety of
important AI tasks such as (dynamic) relational models and boolean model
counting, and that significant efficiency gains are obtainable, often by orders
of magnitude.
| [
{
"version": "v1",
"created": "Wed, 9 May 2012 15:37:58 GMT"
}
] | 1,336,953,600,000 | [
[
"Kersting",
"Kristian",
""
],
[
"Ahmadi",
"Babak",
""
],
[
"Natarajan",
"Sriraam",
""
]
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.