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1301.3869 | Daphne Koller | Daphne Koller, Ron Parr | Policy Iteration for Factored MDPs | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-326-334 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many large MDPs can be represented compactly using a dynamic Bayesian
network. Although the structure of the value function does not retain the
structure of the process, recent work has shown that value functions in
factored MDPs can often be approximated well using a decomposed value function:
a linear combination of <I>restricted</I> basis functions, each of which refers
only to a small subset of variables. An approximate value function for a
particular policy can be computed using approximate dynamic programming, but
this approach (and others) can only produce an approximation relative to a
distance metric which is weighted by the stationary distribution of the current
policy. This type of weighted projection is ill-suited to policy improvement.
We present a new approach to value determination, that uses a simple
closed-form computation to directly compute a least-squares decomposed
approximation to the value function <I>for any weights</I>. We then use this
value determination algorithm as a subroutine in a policy iteration process. We
show that, under reasonable restrictions, the policies induced by a factored
value function are compactly represented, and can be manipulated efficiently in
a policy iteration process. We also present a method for computing error bounds
for decomposed value functions using a variable-elimination algorithm for
function optimization. The complexity of all of our algorithms depends on the
factorization of system dynamics and of the approximate value function.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:51:06 GMT"
}
] | 1,358,467,200,000 | [
[
"Koller",
"Daphne",
""
],
[
"Parr",
"Ron",
""
]
] |
1301.3872 | Tsai-Ching Lu | Tsai-Ching Lu, Marek J. Druzdzel, Tze-Yun Leong | Causal Mechanism-based Model Construction | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-353-362 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a framework for building graphical causal model that is based on
the concept of causal mechanisms. Causal models are intuitive for human users
and, more importantly, support the prediction of the effect of manipulation. We
describe an implementation of the proposed framework as an interactive model
construction module, ImaGeNIe, in SMILE (Structural Modeling, Inference, and
Learning Engine) and in GeNIe (SMILE's Windows user interface).
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:51:18 GMT"
}
] | 1,358,467,200,000 | [
[
"Lu",
"Tsai-Ching",
""
],
[
"Druzdzel",
"Marek J.",
""
],
[
"Leong",
"Tze-Yun",
""
]
] |
1301.3873 | Thomas Lukasiewicz | Thomas Lukasiewicz | Credal Networks under Maximum Entropy | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-363-370 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We apply the principle of maximum entropy to select a unique joint
probability distribution from the set of all joint probability distributions
specified by a credal network. In detail, we start by showing that the unique
joint distribution of a Bayesian tree coincides with the maximum entropy model
of its conditional distributions. This result, however, does not hold anymore
for general Bayesian networks. We thus present a new kind of maximum entropy
models, which are computed sequentially. We then show that for all general
Bayesian networks, the sequential maximum entropy model coincides with the
unique joint distribution. Moreover, we apply the new principle of sequential
maximum entropy to interval Bayesian networks and more generally to credal
networks. We especially show that this application is equivalent to a number of
small local entropy maximizations.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:51:22 GMT"
}
] | 1,358,467,200,000 | [
[
"Lukasiewicz",
"Thomas",
""
]
] |
1301.3874 | Peter McBurney | Peter McBurney, Simon Parsons | Risk Agoras: Dialectical Argumentation for Scientific Reasoning | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-371-379 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a formal framework for intelligent systems which can reason about
scientific domains, in particular about the carcinogenicity of chemicals, and
we study its properties. Our framework is grounded in a philosophy of
scientific enquiry and discourse, and uses a model of dialectical
argumentation. The formalism enables representation of scientific uncertainty
and conflict in a manner suitable for qualitative reasoning about the domain.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:51:26 GMT"
}
] | 1,358,467,200,000 | [
[
"McBurney",
"Peter",
""
],
[
"Parsons",
"Simon",
""
]
] |
1301.3876 | Brian Milch | Brian Milch, Daphne Koller | Probabilistic Models for Agents' Beliefs and Decisions | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-389-396 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many applications of intelligent systems require reasoning about the mental
states of agents in the domain. We may want to reason about an agent's beliefs,
including beliefs about other agents; we may also want to reason about an
agent's preferences, and how his beliefs and preferences relate to his
behavior. We define a probabilistic epistemic logic (PEL) in which belief
statements are given a formal semantics, and provide an algorithm for asserting
and querying PEL formulas in Bayesian networks. We then show how to reason
about an agent's behavior by modeling his decision process as an influence
diagram and assuming that he behaves rationally. PEL can then be used for
reasoning from an agent's observed actions to conclusions about other aspects
of the domain, including unobserved domain variables and the agent's mental
states.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:51:34 GMT"
}
] | 1,358,467,200,000 | [
[
"Milch",
"Brian",
""
],
[
"Koller",
"Daphne",
""
]
] |
1301.3879 | Thomas D. Nielsen | Thomas D. Nielsen, Finn Verner Jensen | Representing and Solving Asymmetric Bayesian Decision Problems | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-416-425 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper deals with the representation and solution of asymmetric Bayesian
decision problems. We present a formal framework, termed asymmetric influence
diagrams, that is based on the influence diagram and allows an efficient
representation of asymmetric decision problems. As opposed to existing
frameworks, the asymmetric influece diagram primarily encodes asymmetry at the
qualitative level and it can therefore be read directly from the model. We give
an algorithm for solving asymmetric influence diagrams. The algorithm initially
decomposes the asymmetric decision problem into a structure of symmetric
subproblems organized as a tree. A solution to the decision problem can then be
found by propagating from the leaves toward the root using existing evaluation
methods to solve the sub-problems.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:51:46 GMT"
}
] | 1,358,467,200,000 | [
[
"Nielsen",
"Thomas D.",
""
],
[
"Jensen",
"Finn Verner",
""
]
] |
1301.3880 | Thomas D. Nielsen | Thomas D. Nielsen, Pierre-Henri Wuillemin, Finn Verner Jensen, Uffe
Kj{\ae}rulff | Using ROBDDs for Inference in Bayesian Networks with Troubleshooting as
an Example | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-426-435 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | When using Bayesian networks for modelling the behavior of man-made
machinery, it usually happens that a large part of the model is deterministic.
For such Bayesian networks deterministic part of the model can be represented
as a Boolean function, and a central part of belief updating reduces to the
task of calculating the number of satisfying configurations in a Boolean
function. In this paper we explore how advances in the calculation of Boolean
functions can be adopted for belief updating, in particular within the context
of troubleshooting. We present experimental results indicating a substantial
speed-up compared to traditional junction tree propagation.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:51:50 GMT"
}
] | 1,358,467,200,000 | [
[
"Nielsen",
"Thomas D.",
""
],
[
"Wuillemin",
"Pierre-Henri",
""
],
[
"Jensen",
"Finn Verner",
""
],
[
"Kjærulff",
"Uffe",
""
]
] |
1301.3881 | Dennis Nilsson | Dennis Nilsson, Steffen L. Lauritzen | Evaluating Influence Diagrams using LIMIDs | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-436-445 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a new approach to the solution of decision problems formulated as
influence diagrams. The approach converts the influence diagram into a simpler
structure, the LImited Memory Influence Diagram (LIMID), where only the
requisite information for the computation of optimal policies is depicted.
Because the requisite information is explicitly represented in the diagram, the
evaluation procedure can take advantage of it. In this paper we show how to
convert an influence diagram to a LIMID and describe the procedure for finding
an optimal strategy. Our approach can yield significant savings of memory and
computational time when compared to traditional methods.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:51:54 GMT"
}
] | 1,358,467,200,000 | [
[
"Nilsson",
"Dennis",
""
],
[
"Lauritzen",
"Steffen L.",
""
]
] |
1301.3883 | Tim Paek | Tim Paek, Eric J. Horvitz | Conversation as Action Under Uncertainty | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-455-464 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Conversations abound with uncetainties of various kinds. Treating
conversation as inference and decision making under uncertainty, we propose a
task independent, multimodal architecture for supporting robust continuous
spoken dialog called Quartet. We introduce four interdependent levels of
analysis, and describe representations, inference procedures, and decision
strategies for managing uncertainties within and between the levels. We
highlight the approach by reviewing interactions between a user and two spoken
dialog systems developed using the Quartet architecture: Prsenter, a prototype
system for navigating Microsoft PowerPoint presentations, and the Bayesian
Receptionist, a prototype system for dealing with tasks typically handled by
front desk receptionists at the Microsoft corporate campus.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:52:02 GMT"
}
] | 1,358,467,200,000 | [
[
"Paek",
"Tim",
""
],
[
"Horvitz",
"Eric J.",
""
]
] |
1301.3887 | Pascal Poupart | Pascal Poupart, Craig Boutilier | Value-Directed Belief State Approximation for POMDPs | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-497-506 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the problem belief-state monitoring for the purposes of
implementing a policy for a partially-observable Markov decision process
(POMDP), specifically how one might approximate the belief state. Other schemes
for belief-state approximation (e.g., based on minimixing a measures such as
KL-diveregence between the true and estimated state) are not necessarily
appropriate for POMDPs. Instead we propose a framework for analyzing
value-directed approximation schemes, where approximation quality is determined
by the expected error in utility rather than by the error in the belief state
itself. We propose heuristic methods for finding good projection schemes for
belief state estimation - exhibiting anytime characteristics - given a POMDP
value fucntion. We also describe several algorithms for constructing bounds on
the error in decision quality (expected utility) associated with acting in
accordance with a given belief state approximation.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:52:18 GMT"
}
] | 1,358,467,200,000 | [
[
"Poupart",
"Pascal",
""
],
[
"Boutilier",
"Craig",
""
]
] |
1301.3888 | David V. Pynadath | David V. Pynadath, Michael P. Wellman | Probabilistic State-Dependent Grammars for Plan Recognition | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-507-514 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Techniques for plan recognition under uncertainty require a stochastic model
of the plan-generation process. We introduce Probabilistic State-Dependent
Grammars (PSDGs) to represent an agent's plan-generation process. The PSDG
language model extends probabilistic context-free grammars (PCFGs) by allowing
production probabilities to depend on an explicit model of the planning agent's
internal and external state. Given a PSDG description of the plan-generation
process, we can then use inference algorithms that exploit the particular
independence properties of the PSDG language to efficiently answer
plan-recognition queries. The combination of the PSDG language model and
inference algorithms extends the range of plan-recognition domains for which
practical probabilistic inference is possible, as illustrated by applications
in traffic monitoring and air combat.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:52:22 GMT"
}
] | 1,358,467,200,000 | [
[
"Pynadath",
"David V.",
""
],
[
"Wellman",
"Michael P.",
""
]
] |
1301.3889 | Silja Renooij | Silja Renooij, Linda C. van der Gaag, Simon Parsons, Shaw Green | Pivotal Pruning of Trade-offs in QPNs | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-515-522 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Qualitative probabilistic networks have been designed for probabilistic
reasoning in a qualitative way. Due to their coarse level of representation
detail, qualitative probabilistic networks do not provide for resolving
trade-offs and typically yield ambiguous results upon inference. We present an
algorithm for computing more insightful results for unresolved trade-offs. The
algorithm builds upon the idea of using pivots to zoom in on the trade-offs and
identifying the information that would serve to resolve them.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:52:25 GMT"
}
] | 1,358,467,200,000 | [
[
"Renooij",
"Silja",
""
],
[
"van der Gaag",
"Linda C.",
""
],
[
"Parsons",
"Simon",
""
],
[
"Green",
"Shaw",
""
]
] |
1301.3893 | Claus Skaanning | Claus Skaanning | A Knowledge Acquisition Tool for Bayesian-Network Troubleshooters | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-549-557 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes a domain-specific knowledge acquisition tool for
intelligent automated troubleshooters based on Bayesian networks. No Bayesian
network knowledge is required to use the tool, and troubleshooting information
can be specified as natural and intuitive as possible. Probabilities can be
specified in the direction that is most natural to the domain expert. Thus, the
knowledge acquisition efficiently removes the traditional knowledge acquisition
bottleneck of Bayesian networks.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:52:41 GMT"
}
] | 1,358,467,200,000 | [
[
"Skaanning",
"Claus",
""
]
] |
1301.3894 | Harald Steck | Harald Steck | On the Use of Skeletons when Learning in Bayesian Networks | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-558-565 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we present a heuristic operator which aims at simultaneously
optimizing the orientations of all the edges in an intermediate Bayesian
network structure during the search process. This is done by alternating
between the space of directed acyclic graphs (DAGs) and the space of skeletons.
The found orientations of the edges are based on a scoring function rather than
on induced conditional independences. This operator can be used as an extension
to commonly employed search strategies. It is evaluated in experiments with
artificial and real-world data.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:52:45 GMT"
}
] | 1,358,467,200,000 | [
[
"Steck",
"Harald",
""
]
] |
1301.3898 | Jin Tian | Jin Tian, Judea Pearl | Probabilities of Causation: Bounds and Identification | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-589-598 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper deals with the problem of estimating the probability that one
event was a cause of another in a given scenario. Using structural-semantical
definitions of the probabilities of necessary or sufficient causation (or
both), we show how to optimally bound these quantities from data obtained in
experimental and observational studies, making minimal assumptions concerning
the data-generating process. In particular, we strengthen the results of Pearl
(1999) by weakening the data-generation assumptions and deriving theoretically
sharp bounds on the probabilities of causation. These results delineate
precisely how empirical data can be used both in settling questions of
attribution and in solving attribution-related problems of decision making.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:53:00 GMT"
}
] | 1,358,467,200,000 | [
[
"Tian",
"Jin",
""
],
[
"Pearl",
"Judea",
""
]
] |
1301.3900 | Jirina Vejnarova | Jirina Vejnarova | Conditional Independence and Markov Properties in Possibility Theory | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-609-616 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Conditional independence and Markov properties are powerful tools allowing
expression of multidimensional probability distributions by means of
low-dimensional ones. As multidimensional possibilistic models have been
studied for several years, the demand for analogous tools in possibility theory
seems to be quite natural. This paper is intended to be a promotion of de
Cooman's measure-theoretic approcah to possibility theory, as this approach
allows us to find analogies to many important results obtained in probabilistic
framework. First, we recall semi-graphoid properties of conditional
possibilistic independence, parameterized by a continuous t-norm, and find
sufficient conditions for a class of Archimedean t-norms to have the graphoid
property. Then we introduce Markov properties and factorization of possibility
distrubtions (again parameterized by a continuous t-norm) and find the
relationships between them. These results are accompanied by a number of
conterexamples, which show that the assumptions of specific theorems are
substantial.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:53:09 GMT"
}
] | 1,358,467,200,000 | [
[
"Vejnarova",
"Jirina",
""
]
] |
1301.3903 | Frank Wittig | Frank Wittig, Anthony Jameson | Exploiting Qualitative Knowledge in the Learning of Conditional
Probabilities of Bayesian Networks | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-644-652 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Algorithms for learning the conditional probabilities of Bayesian networks
with hidden variables typically operate within a high-dimensional search space
and yield only locally optimal solutions. One way of limiting the search space
and avoiding local optima is to impose qualitative constraints that are based
on background knowledge concerning the domain. We present a method for
integrating formal statements of qualitative constraints into two learning
algorithms, APN and EM. In our experiments with synthetic data, this method
yielded networks that satisfied the constraints almost perfectly. The accuracy
of the learned networks was consistently superior to that of corresponding
networks learned without constraints. The exploitation of qualitative
constraints therefore appears to be a promising way to increase both the
interpretability and the accuracy of learned Bayesian networks with known
structure.
| [
{
"version": "v1",
"created": "Wed, 16 Jan 2013 15:53:24 GMT"
}
] | 1,358,467,200,000 | [
[
"Wittig",
"Frank",
""
],
[
"Jameson",
"Anthony",
""
]
] |
1301.4272 | Marco Correia | Marco Correia and Pedro Barahona | View-based propagation of decomposable constraints | The final publication is available at link.springer.com | null | 10.1007/s10601-013-9140-8 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Constraints that may be obtained by composition from simpler constraints are
present, in some way or another, in almost every constraint program. The
decomposition of such constraints is a standard technique for obtaining an
adequate propagation algorithm from a combination of propagators designed for
simpler constraints. The decomposition approach is appealing in several ways.
Firstly because creating a specific propagator for every constraint is clearly
infeasible since the number of constraints is infinite. Secondly, because
designing a propagation algorithm for complex constraints can be very
challenging. Finally, reusing existing propagators allows to reduce the size of
code to be developed and maintained. Traditionally, constraint solvers
automatically decompose constraints into simpler ones using additional
auxiliary variables and propagators, or expect the users to perform such
decomposition themselves, eventually leading to the same propagation model. In
this paper we explore views, an alternative way to create efficient propagators
for such constraints in a modular, simple and correct way, which avoids the
introduction of auxiliary variables and propagators.
| [
{
"version": "v1",
"created": "Thu, 17 Jan 2013 23:37:47 GMT"
}
] | 1,358,726,400,000 | [
[
"Correia",
"Marco",
""
],
[
"Barahona",
"Pedro",
""
]
] |
1301.4430 | Haiqin Wang | Haiqin Wang, Marek J. Druzdzel | User Interface Tools for Navigation in Conditional Probability Tables
and Elicitation of Probabilities in Bayesian Networks | Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000) | null | null | UAI-P-2000-PG-617-625 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Elicitation of probabilities is one of the most laborious tasks in building
decision-theoretic models, and one that has so far received only moderate
attention in decision-theoretic systems. We propose a set of user interface
tools for graphical probabilistic models, focusing on two aspects of
probability elicitation: (1) navigation through conditional probability tables
and (2) interactive graphical assessment of discrete probability distributions.
We propose two new graphical views that aid navigation in very large
conditional probability tables: the CPTree (Conditional Probability Tree) and
the SCPT (shrinkable Conditional Probability Table). Based on what is known
about graphical presentation of quantitative data to humans, we offer several
useful enhancements to probability wheel and bar graph, including different
chart styles and options that can be adapted to user preferences and needs. We
present the results of a simple usability study that proves the value of the
proposed tools.
| [
{
"version": "v1",
"created": "Fri, 18 Jan 2013 16:50:44 GMT"
}
] | 1,358,726,400,000 | [
[
"Wang",
"Haiqin",
""
],
[
"Druzdzel",
"Marek J.",
""
]
] |
1301.4604 | Nando de Freitas | Nando de Freitas and Kevin Murphy | Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial
Intelligence (2012) | null | null | null | UAI2012 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This is the Proceedings of the Twenty-Eighth Conference on Uncertainty in
Artificial Intelligence, which was held on Catalina Island, CA August 14-18
2012.
| [
{
"version": "v1",
"created": "Sat, 19 Jan 2013 22:32:52 GMT"
},
{
"version": "v2",
"created": "Thu, 28 Aug 2014 04:31:38 GMT"
}
] | 1,409,270,400,000 | [
[
"de Freitas",
"Nando",
""
],
[
"Murphy",
"Kevin",
""
]
] |
1301.4606 | Christopher Meek | Christopher Meek and Uffe Kjaerulff | Proceedings of the Nineteenth Conference on Uncertainty in Artificial
Intelligence (2003) | null | null | null | UAI2003 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This is the Proceedings of the Nineteenth Conference on Uncertainty in
Artificial Intelligence, which was held in Acapulco, Mexico, August 7-10 2003
| [
{
"version": "v1",
"created": "Sat, 19 Jan 2013 23:12:33 GMT"
},
{
"version": "v2",
"created": "Thu, 28 Aug 2014 04:18:59 GMT"
}
] | 1,409,270,400,000 | [
[
"Meek",
"Christopher",
""
],
[
"Kjaerulff",
"Uffe",
""
]
] |
1301.4607 | John Breese | John Breese and Daphne Koller | Proceedings of the Seventeenth Conference on Uncertainty in Artificial
Intelligence (2001) | null | null | null | UAI2001 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This is the Proceedings of the Seventeenth Conference on Uncertainty in
Artificial Intelligence, which was held in Seattle, WA, August 2-5 2001
| [
{
"version": "v1",
"created": "Sat, 19 Jan 2013 23:16:59 GMT"
},
{
"version": "v2",
"created": "Thu, 28 Aug 2014 04:16:28 GMT"
}
] | 1,409,270,400,000 | [
[
"Breese",
"John",
""
],
[
"Koller",
"Daphne",
""
]
] |
1301.4608 | Adnan Darwiche | Adnan Darwiche and Nir Friedman | Proceedings of the Eighteenth Conference on Uncertainty in Artificial
Intelligence (2002) | null | null | null | UAI2002 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This is the Proceedings of the Eighteenth Conference on Uncertainty in
Artificial Intelligence, which was held in Alberta, Canada, August 1-4 2002
| [
{
"version": "v1",
"created": "Sat, 19 Jan 2013 23:17:26 GMT"
},
{
"version": "v2",
"created": "Thu, 28 Aug 2014 04:17:50 GMT"
}
] | 1,409,270,400,000 | [
[
"Darwiche",
"Adnan",
""
],
[
"Friedman",
"Nir",
""
]
] |
1301.4659 | Firoj Parwej Dr. | Firoj Parwej | English Sentence Recognition using Artificial Neural Network through
Mouse-based Gestures | 6 Pages, 7 Figures. arXiv admin note: text overlap with
arXiv:1007.0627 by other authors without attribution | International Journal of Computer Applications (IJCA)USA, Volume
61, No.17, January 2013 ISSN 0975 - 8887, http://www.ijcaonline.org,
http://www.ijcaonline.org/archives/volume61/number17/10023-4998 | 10.5120/10023-4998 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Handwriting is one of the most important means of daily communication.
Although the problem of handwriting recognition has been considered for more
than 60 years there are still many open issues, especially in the task of
unconstrained handwritten sentence recognition. This paper focuses on the
automatic system that recognizes continuous English sentence through a
mouse-based gestures in real-time based on Artificial Neural Network. The
proposed Artificial Neural Network is trained using the traditional
backpropagation algorithm for self supervised neural network which provides the
system with great learning ability and thus has proven highly successful in
training for feed-forward Artificial Neural Network. The designed algorithm is
not only capable of translating discrete gesture moves, but also continuous
gestures through the mouse. In this paper we are using the efficient neural
network approach for recognizing English sentence drawn by mouse. This approach
shows an efficient way of extracting the boundary of the English Sentence and
specifies the area of the recognition English sentence where it has been drawn
in an image and then used Artificial Neural Network to recognize the English
sentence. The proposed approach English sentence recognition (ESR) system is
designed and tested successfully. Experimental results show that the higher
speed and accuracy were examined.
| [
{
"version": "v1",
"created": "Sun, 20 Jan 2013 14:13:22 GMT"
}
] | 1,358,812,800,000 | [
[
"Parwej",
"Firoj",
""
]
] |
1301.4991 | Christophe Cruz | Helmi Ben Hmida (i3mainz), Christophe Cruz (Le2i), Frank Boochs
(i3mainz), Christophe Nicolle (Le2i) | Knowledge Base Approach for 3D Objects Detection in Point Clouds Using
3D Processing and Specialists Knowledge | ISSN: 1942-2679. arXiv admin note: text overlap with arXiv:1301.4783 | International Journal On Advances in Intelligent Systems 5, 1 et 2
(2012) 1-14 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a knowledge-based detection of objects approach using the
OWL ontology language, the Semantic Web Rule Language, and 3D processing
built-ins aiming at combining geometrical analysis of 3D point clouds and
specialist's knowledge. Here, we share our experience regarding the creation of
3D semantic facility model out of unorganized 3D point clouds. Thus, a
knowledge-based detection approach of objects using the OWL ontology language
is presented. This knowledge is used to define SWRL detection rules. In
addition, the combination of 3D processing built-ins and topological Built-Ins
in SWRL rules allows a more flexible and intelligent detection, and the
annotation of objects contained in 3D point clouds. The created WiDOP prototype
takes a set of 3D point clouds as input, and produces as output a populated
ontology corresponding to an indexed scene visualized within VRML language. The
context of the study is the detection of railway objects materialized within
the Deutsche Bahn scene such as signals, technical cupboards, electric poles,
etc. Thus, the resulting enriched and populated ontology, that contains the
annotations of objects in the point clouds, is used to feed a GIS system or an
IFC file for architecture purposes.
| [
{
"version": "v1",
"created": "Mon, 21 Jan 2013 12:42:17 GMT"
}
] | 1,358,899,200,000 | [
[
"Hmida",
"Helmi Ben",
"",
"i3mainz"
],
[
"Cruz",
"Christophe",
"",
"Le2i"
],
[
"Boochs",
"Frank",
"",
"i3mainz"
],
[
"Nicolle",
"Christophe",
"",
"Le2i"
]
] |
1301.4992 | Christophe Cruz | Helmi Ben Hmida (i3mainz), Christophe Cruz (Le2i), Frank Boochs
(i3mainz), Christophe Nicolle (Le2i) | From 9-IM Topological Operators to Qualitative Spatial Relations using
3D Selective Nef Complexes and Logic Rules for bodies | arXiv admin note: substantial text overlap with arXiv:1301.4780 | International Conference on Knowledge Engineering and Ontology
Development, Barcelone : Spain (2012) | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a method to compute automatically topological relations
using SWRL rules. The calculation of these rules is based on the definition of
a Selective Nef Complexes Nef Polyhedra structure generated from standard
Polyhedron. The Selective Nef Complexes is a data model providing a set of
binary Boolean operators such as Union, Difference, Intersection and Symmetric
difference, and unary operators such as Interior, Closure and Boundary. In this
work, these operators are used to compute topological relations between objects
defined by the constraints of the 9 Intersection Model (9-IM) from Egenhofer.
With the help of these constraints, we defined a procedure to compute the
topological relations on Nef polyhedra. These topological relationships are
Disjoint, Meets, Contains, Inside, Covers, CoveredBy, Equals and Overlaps, and
defined in a top-level ontology with a specific semantic definition on relation
such as Transitive, Symmetric, Asymmetric, Functional, Reflexive, and
Irreflexive. The results of the computation of topological relationships are
stored in an OWL-DL ontology allowing after what to infer on these new
relationships between objects. In addition, logic rules based on the Semantic
Web Rule Language allows the definition of logic programs that define which
topological relationships have to be computed on which kind of objects with
specific attributes. For instance, a "Building" that overlaps a "Railway" is a
"RailStation".
| [
{
"version": "v1",
"created": "Mon, 21 Jan 2013 12:43:38 GMT"
}
] | 1,358,899,200,000 | [
[
"Hmida",
"Helmi Ben",
"",
"i3mainz"
],
[
"Cruz",
"Christophe",
"",
"Le2i"
],
[
"Boochs",
"Frank",
"",
"i3mainz"
],
[
"Nicolle",
"Christophe",
"",
"Le2i"
]
] |
1301.5946 | Lu\'is Filipe Te\'ofilo | Lu\'is Filipe Te\'ofilo, Lu\'is Paulo Reis, Henrique Lopes Cardoso,
Dinis F\'elix, Rui S\^eca, Jo\~ao Ferreira, Pedro Mendes, Nuno Cruz, Vitor
Pereira, Nuno Passos | Computer Poker Research at LIACC | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Computer Poker's unique characteristics present a well-suited challenge for
research in artificial intelligence. For that reason, and due to the Poker's
market increase in popularity in Portugal since 2008, several members of LIACC
have researched in this field. Several works were published as papers and
master theses and more recently a member of LIACC engaged on a research in this
area as a Ph.D. thesis in order to develop a more extensive and in-depth work.
This paper describes the existing research in LIACC about Computer Poker, with
special emphasis on the completed master's theses and plans for future work.
This paper means to present a summary of the lab's work to the research
community in order to encourage the exchange of ideas with other labs /
individuals. LIACC hopes this will improve research in this area so as to reach
the goal of creating an agent that surpasses the best human players.
| [
{
"version": "v1",
"created": "Fri, 25 Jan 2013 01:56:03 GMT"
}
] | 1,359,331,200,000 | [
[
"Teófilo",
"Luís Filipe",
""
],
[
"Reis",
"Luís Paulo",
""
],
[
"Cardoso",
"Henrique Lopes",
""
],
[
"Félix",
"Dinis",
""
],
[
"Sêca",
"Rui",
""
],
[
"Ferreira",
"João",
""
],
[
"Mendes",
"Pedro",
""
],
[
"Cruz",
"Nuno",
""
],
[
"Pereira",
"Vitor",
""
],
[
"Passos",
"Nuno",
""
]
] |
1301.6011 | D P Acharjya Ph.D | B.K.Tripathy, D.P.Acharjya and V.Cynthya | A Framework for Intelligent Medical Diagnosis using Rough Set with
Formal Concept Analysis | 22 pages | International Journal of Artificial Intelligence & Applications
(IJAIA), Vol.2, No.2, April 2011 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Medical diagnosis process vary in the degree to which they attempt to deal
with different complicating aspects of diagnosis such as relative importance of
symptoms, varied symptom pattern and the relation between diseases them selves.
Based on decision theory, in the past many mathematical models such as crisp
set, probability distribution, fuzzy set, intuitionistic fuzzy set were
developed to deal with complicating aspects of diagnosis. But, many such models
are failed to include important aspects of the expert decisions. Therefore, an
effort has been made to process inconsistencies in data being considered by
Pawlak with the introduction of rough set theory. Though rough set has major
advantages over the other methods, but it generates too many rules that create
many difficulties while taking decisions. Therefore, it is essential to
minimize the decision rules. In this paper, we use two processes such as pre
process and post process to mine suitable rules and to explore the relationship
among the attributes. In pre process we use rough set theory to mine suitable
rules, whereas in post process we use formal concept analysis from these
suitable rules to explore better knowledge and most important factors affecting
the decision making.
| [
{
"version": "v1",
"created": "Fri, 25 Jan 2013 11:24:05 GMT"
}
] | 1,359,331,200,000 | [
[
"Tripathy",
"B. K.",
""
],
[
"Acharjya",
"D. P.",
""
],
[
"Cynthya",
"V.",
""
]
] |
1301.6262 | Sim-Hui Tee | Sim-Hui Tee | Developing Parallel Dependency Graph In Improving Game Balancing | 5 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The dependency graph is a data architecture that models all the dependencies
between the different types of assets in the game. It depicts the
dependency-based relationships between the assets of a game. For example, a
player must construct an arsenal before he can build weapons. It is vital that
the dependency graph of a game is designed logically to ensure a logical
sequence of game play. However, a mere logical dependency graph is not
sufficient in sustaining the players' enduring interests in a game, which
brings the problem of game balancing into picture. The issue of game balancing
arises when the players do not feel the chances of winning the game over their
AI opponents who are more skillful in the game play. At the current state of
research, the architecture of dependency graph is monolithic for the players.
The sequence of asset possession is always foreseeable because there is only a
single dependency graph. Game balancing is impossible when the assets of AI
players are overwhelmingly outnumbering that of human players. This paper
proposes a parallel architecture of dependency graph for the AI players and
human players. Instead of having a single dependency graph, a parallel
architecture is proposed where the dependency graph of AI player is adjustable
with that of human player using a support dependency as a game balancing
mechanism. This paper exhibits that the parallel dependency graph helps to
improve game balancing.
| [
{
"version": "v1",
"created": "Sat, 26 Jan 2013 14:41:03 GMT"
}
] | 1,359,417,600,000 | [
[
"Tee",
"Sim-Hui",
""
]
] |
1301.6359 | Alexander Serov | Alexander Serov | Subjective Reality and Strong Artificial Intelligence | 10 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The main prospective aim of modern research related to Artificial
Intelligence is the creation of technical systems that implement the idea of
Strong Intelligence. According our point of view the path to the development of
such systems comes through the research in the field related to perceptions.
Here we formulate the model of the perception of external world which may be
used for the description of perceptual activity of intelligent beings. We
consider a number of issues related to the development of the set of patterns
which will be used by the intelligent system when interacting with environment.
The key idea of the presented perception model is the idea of subjective
reality. The principle of the relativity of perceived world is formulated. It
is shown that this principle is the immediate consequence of the idea of
subjective reality. In this paper we show how the methodology of subjective
reality may be used for the creation of different types of Strong AI systems.
| [
{
"version": "v1",
"created": "Sun, 27 Jan 2013 14:29:04 GMT"
},
{
"version": "v2",
"created": "Tue, 29 Jan 2013 17:32:23 GMT"
}
] | 1,359,504,000,000 | [
[
"Serov",
"Alexander",
""
]
] |
1301.6675 | Gustavo Arroyo-Figueroa | Gustavo Arroyo-Figueroa, Luis Enrique Sucar | A Temporal Bayesian Network for Diagnosis and Prediction | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-13-20 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Diagnosis and prediction in some domains, like medical and industrial
diagnosis, require a representation that combines uncertainty management and
temporal reasoning. Based on the fact that in many cases there are few state
changes in the temporal range of interest, we propose a novel representation
called Temporal Nodes Bayesian Networks (TNBN). In a TNBN each node represents
an event or state change of a variable, and an arc corresponds to a
causal-temporal relationship. The temporal intervals can differ in number and
size for each temporal node, so this allows multiple granularity. Our approach
is contrasted with a dynamic Bayesian network for a simple medical example. An
empirical evaluation is presented for a more complex problem, a subsystem of a
fossil power plant, in which this approach is used for fault diagnosis and
prediction with good results.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 15:56:38 GMT"
}
] | 1,359,504,000,000 | [
[
"Arroyo-Figueroa",
"Gustavo",
""
],
[
"Sucar",
"Luis Enrique",
""
]
] |
1301.6679 | Salem Benferhat | Salem Benferhat, Didier Dubois, Laurent Garcia, Henri Prade | Possibilistic logic bases and possibilistic graphs | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-57-64 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Possibilistic logic bases and possibilistic graphs are two different
frameworks of interest for representing knowledge. The former stratifies the
pieces of knowledge (expressed by logical formulas) according to their level of
certainty, while the latter exhibits relationships between variables. The two
types of representations are semantically equivalent when they lead to the same
possibility distribution (which rank-orders the possible interpretations). A
possibility distribution can be decomposed using a chain rule which may be
based on two different kinds of conditioning which exist in possibility theory
(one based on product in a numerical setting, one based on minimum operation in
a qualitative setting). These two types of conditioning induce two kinds of
possibilistic graphs. In both cases, a translation of these graphs into
possibilistic bases is provided. The converse translation from a possibilistic
knowledge base into a min-based graph is also described.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 15:56:55 GMT"
}
] | 1,359,504,000,000 | [
[
"Benferhat",
"Salem",
""
],
[
"Dubois",
"Didier",
""
],
[
"Garcia",
"Laurent",
""
],
[
"Prade",
"Henri",
""
]
] |
1301.6680 | Magnus Boman | Magnus Boman, Paul Davidsson, Hakan L. Younes | Artificial Decision Making Under Uncertainty in Intelligent Buildings | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-65-70 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Our hypothesis is that by equipping certain agents in a multi-agent system
controlling an intelligent building with automated decision support, two
important factors will be increased. The first is energy saving in the
building. The second is customer value---how the people in the building
experience the effects of the actions of the agents. We give evidence for the
truth of this hypothesis through experimental findings related to tools for
artificial decision making. A number of assumptions related to agent control,
through monitoring and delegation of tasks to other kinds of agents, of rooms
at a test site are relaxed. Each assumption controls at least one uncertainty
that complicates considerably the procedures for selecting actions part of each
such agent. We show that in realistic decision situations, room-controlling
agents can make bounded rational decisions even under dynamic real-time
constraints. This result can be, and has been, generalized to other domains
with even harsher time constraints.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 15:56:59 GMT"
}
] | 1,359,504,000,000 | [
[
"Boman",
"Magnus",
""
],
[
"Davidsson",
"Paul",
""
],
[
"Younes",
"Hakan L.",
""
]
] |
1301.6681 | Craig Boutilier | Craig Boutilier, Ronen I. Brafman, Holger H. Hoos, David L. Poole | Reasoning With Conditional Ceteris Paribus Preference Statem | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-71-80 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In many domains it is desirable to assess the preferences of users in a
qualitative rather than quantitative way. Such representations of qualitative
preference orderings form an importnat component of automated decision tools.
We propose a graphical representation of preferences that reflects conditional
dependence and independence of preference statements under a ceteris paribus
(all else being equal) interpretation. Such a representation is ofetn compact
and arguably natural. We describe several search algorithms for dominance
testing based on this representation; these algorithms are quite effective,
especially in specific network topologies, such as chain-and tree- structured
networks, as well as polytrees.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 15:57:03 GMT"
}
] | 1,359,504,000,000 | [
[
"Boutilier",
"Craig",
""
],
[
"Brafman",
"Ronen I.",
""
],
[
"Hoos",
"Holger H.",
""
],
[
"Poole",
"David L.",
""
]
] |
1301.6686 | Gregory F. Cooper | Gregory F. Cooper, Changwon Yoo | Causal Discovery from a Mixture of Experimental and Observational Data | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-116-125 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes a Bayesian method for combining an arbitrary mixture of
observational and experimental data in order to learn causal Bayesian networks.
Observational data are passively observed. Experimental data, such as that
produced by randomized controlled trials, result from the experimenter
manipulating one or more variables (typically randomly) and observing the
states of other variables. The paper presents a Bayesian method for learning
the causal structure and parameters of the underlying causal process that is
generating the data, given that (1) the data contains a mixture of
observational and experimental case records, and (2) the causal process is
modeled as a causal Bayesian network. This learning method was applied using as
input various mixtures of experimental and observational data that were
generated from the ALARM causal Bayesian network. In these experiments, the
absolute and relative quantities of experimental and observational data were
varied systematically. For each of these training datasets, the learning method
was applied to predict the causal structure and to estimate the causal
parameters that exist among randomly selected pairs of nodes in ALARM that are
not confounded. The paper reports how these structure predictions and parameter
estimates compare with the true causal structures and parameters as given by
the ALARM network.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 15:57:22 GMT"
}
] | 1,359,504,000,000 | [
[
"Cooper",
"Gregory F.",
""
],
[
"Yoo",
"Changwon",
""
]
] |
1301.6687 | James Cussens | James Cussens | Loglinear models for first-order probabilistic reasoning | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-126-133 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent work on loglinear models in probabilistic constraint logic programming
is applied to first-order probabilistic reasoning. Probabilities are defined
directly on the proofs of atomic formulae, and by marginalisation on the atomic
formulae themselves. We use Stochastic Logic Programs (SLPs) composed of
labelled and unlabelled definite clauses to define the proof probabilities. We
have a conservative extension of first-order reasoning, so that, for example,
there is a one-one mapping between logical and random variables. We show how,
in this framework, Inductive Logic Programming (ILP) can be used to induce the
features of a loglinear model from data. We also compare the presented
framework with other approaches to first-order probabilistic reasoning.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 15:57:26 GMT"
}
] | 1,359,504,000,000 | [
[
"Cussens",
"James",
""
]
] |
1301.6689 | Denver Dash | Denver Dash, Marek J. Druzdzel | A Hybrid Anytime Algorithm for the Constructiion of Causal Models From
Sparse Data | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-142-149 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a hybrid constraint-based/Bayesian algorithm for learning causal
networks in the presence of sparse data. The algorithm searches the space of
equivalence classes of models (essential graphs) using a heuristic based on
conventional constraint-based techniques. Each essential graph is then
converted into a directed acyclic graph and scored using a Bayesian scoring
metric. Two variants of the algorithm are developed and tested using data from
randomly generated networks of sizes from 15 to 45 nodes with data sizes
ranging from 250 to 2000 records. Both variations are compared to, and found to
consistently outperform two variations of greedy search with restarts.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 15:57:34 GMT"
}
] | 1,359,504,000,000 | [
[
"Dash",
"Denver",
""
],
[
"Druzdzel",
"Marek J.",
""
]
] |
1301.6691 | Michael I. Dekhtyar | Michael I. Dekhtyar, Alex Dekhtyar, V. S. Subrahmanian | Hybrid Probabilistic Programs: Algorithms and Complexity | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-160-169 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Hybrid Probabilistic Programs (HPPs) are logic programs that allow the
programmer to explicitly encode his knowledge of the dependencies between
events being described in the program. In this paper, we classify HPPs into
three classes called HPP_1,HPP_2 and HPP_r,r>= 3. For these classes, we provide
three types of results for HPPs. First, we develop algorithms to compute the
set of all ground consequences of an HPP. Then we provide algorithms and
complexity results for the problems of entailment ("Given an HPP P and a query
Q as input, is Q a logical consequence of P?") and consistency ("Given an HPP P
as input, is P consistent?"). Our results provide a fine characterization of
when polynomial algorithms exist for the above problems, and when these
problems become intractable.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 15:57:43 GMT"
}
] | 1,359,504,000,000 | [
[
"Dekhtyar",
"Michael I.",
""
],
[
"Dekhtyar",
"Alex",
""
],
[
"Subrahmanian",
"V. S.",
""
]
] |
1301.6692 | Didier Dubois | Didier Dubois, Michel Grabisch, Henri Prade, Philippe Smets | Assessing the value of a candidate. Comparing belief function and
possibility theories | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-170-177 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The problem of assessing the value of a candidate is viewed here as a
multiple combination problem. On the one hand a candidate can be evaluated
according to different criteria, and on the other hand several experts are
supposed to assess the value of candidates according to each criterion.
Criteria are not equally important, experts are not equally competent or
reliable. Moreover levels of satisfaction of criteria, or levels of confidence
are only assumed to take their values in qualitative scales which are just
linearly ordered. The problem is discussed within two frameworks, the
transferable belief model and the qualitative possibility theory. They
respectively offer a quantitative and a qualitative setting for handling the
problem, providing thus a way to compare the nature of the underlying
assumptions.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 15:57:47 GMT"
}
] | 1,359,504,000,000 | [
[
"Dubois",
"Didier",
""
],
[
"Grabisch",
"Michel",
""
],
[
"Prade",
"Henri",
""
],
[
"Smets",
"Philippe",
""
]
] |
1301.6694 | Helene Fargier | Helene Fargier, Patrice Perny | Qualitative Models for Decision Under Uncertainty without the
Commensurability Assumption | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-188-195 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper investigates a purely qualitative version of Savage's theory for
decision making under uncertainty. Until now, most representation theorems for
preference over acts rely on a numerical representation of utility and
uncertainty where utility and uncertainty are commensurate. Disrupting the
tradition, we relax this assumption and introduce a purely ordinal axiom
requiring that the Decision Maker (DM) preference between two acts only depends
on the relative position of their consequences for each state. Within this
qualitative framework, we determine the only possible form of the decision rule
and investigate some instances compatible with the transitivity of the strict
preference. Finally we propose a mild relaxation of our ordinality axiom,
leaving room for a new family of qualitative decision rules compatible with
transitivity.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 15:57:55 GMT"
}
] | 1,359,504,000,000 | [
[
"Fargier",
"Helene",
""
],
[
"Perny",
"Patrice",
""
]
] |
1301.6699 | Phan H. Giang | Phan H. Giang, Prakash P. Shenoy | On Transformations between Probability and Spohnian Disbelief Functions | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-236-244 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we analyze the relationship between probability and Spohn's
theory for representation of uncertain beliefs. Using the intuitive idea that
the more probable a proposition is, the more believable it is, we study
transformations from probability to Sphonian disbelief and vice-versa. The
transformations described in this paper are different from those described in
the literature. In particular, the former satisfies the principles of ordinal
congruence while the latter does not. Such transformations between probability
and Spohn's calculi can contribute to (1) a clarification of the semantics of
nonprobabilistic degree of uncertain belief, and (2) to a construction of a
decision theory for such calculi. In practice, the transformations will allow a
meaningful combination of more than one calculus in different stages of using
an expert system such as knowledge acquisition, inference, and interpretation
of results.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 15:58:18 GMT"
}
] | 1,359,504,000,000 | [
[
"Giang",
"Phan H.",
""
],
[
"Shenoy",
"Prakash P.",
""
]
] |
1301.6700 | Robert P. Goldman | Robert P. Goldman, Christopher W. Geib, Christopher A. Miller | A New Model of Plan Recognition | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-245-254 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a new abductive, probabilistic theory of plan recognition. This
model differs from previous plan recognition theories in being centered around
a model of plan execution: most previous methods have been based on plans as
formal objects or on rules describing the recognition process. We show that our
new model accounts for phenomena omitted from most previous plan recognition
theories: notably the cumulative effect of a sequence of observations of
partially-ordered, interleaved plans and the effect of context on plan
adoption. The model also supports inferences about the evolution of plan
execution in situations where another agent intervenes in plan execution. This
facility provides support for using plan recognition to build systems that will
intelligently assist a user.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 15:58:22 GMT"
}
] | 1,359,504,000,000 | [
[
"Goldman",
"Robert P.",
""
],
[
"Geib",
"Christopher W.",
""
],
[
"Miller",
"Christopher A.",
""
]
] |
1301.6702 | Vu A. Ha | Vu A. Ha, Peter Haddawy | A Hybrid Approach to Reasoning with Partially Elicited Preference Models | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-263-270 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Classical Decision Theory provides a normative framework for representing and
reasoning about complex preferences. Straightforward application of this theory
to automate decision making is difficult due to high elicitation cost. In
response to this problem, researchers have recently developed a number of
qualitative, logic-oriented approaches for representing and reasoning about
references. While effectively addressing some expressiveness issues, these
logics have not proven powerful enough for building practical automated
decision making systems. In this paper we present a hybrid approach to
preference elicitation and decision making that is grounded in classical
multi-attribute utility theory, but can make effective use of the expressive
power of qualitative approaches. Specifically, assuming a partially specified
multilinear utility function, we show how comparative statements about classes
of decision alternatives can be used to further constrain the utility function
and thus identify sup-optimal alternatives. This work demonstrates that
quantitative and qualitative approaches can be synergistically integrated to
provide effective and flexible decision support.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 15:58:30 GMT"
}
] | 1,359,504,000,000 | [
[
"Ha",
"Vu A.",
""
],
[
"Haddawy",
"Peter",
""
]
] |
1301.6703 | David Harmanec | David Harmanec | Faithful Approximations of Belief Functions | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-271-278 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A conceptual foundation for approximation of belief functions is proposed and
investigated. It is based on the requirements of consistency and closeness. An
optimal approximation is studied. Unfortunately, the computation of the optimal
approximation turns out to be intractable. Hence, various heuristic methods are
proposed and experimantally evaluated both in terms of their accuracy and in
terms of the speed of computation. These methods are compared to the earlier
proposed approximations of belief functions.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 15:58:34 GMT"
}
] | 1,359,504,000,000 | [
[
"Harmanec",
"David",
""
]
] |
1301.6704 | Jesse Hoey | Jesse Hoey, Robert St-Aubin, Alan Hu, Craig Boutilier | SPUDD: Stochastic Planning using Decision Diagrams | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-279-288 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Markov decisions processes (MDPs) are becoming increasing popular as models
of decision theoretic planning. While traditional dynamic programming methods
perform well for problems with small state spaces, structured methods are
needed for large problems. We propose and examine a value iteration algorithm
for MDPs that uses algebraic decision diagrams(ADDs) to represent value
functions and policies. An MDP is represented using Bayesian networks and ADDs
and dynamic programming is applied directly to these ADDs. We demonstrate our
method on large MDPs (up to 63 million states) and show that significant gains
can be had when compared to tree-structured representations (with up to a
thirty-fold reduction in the number of nodes required to represent optimal
value functions).
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 15:58:38 GMT"
}
] | 1,359,504,000,000 | [
[
"Hoey",
"Jesse",
""
],
[
"St-Aubin",
"Robert",
""
],
[
"Hu",
"Alan",
""
],
[
"Boutilier",
"Craig",
""
]
] |
1301.6706 | Michael C. Horsch | Michael C. Horsch, David L. Poole | Estimating the Value of Computation in Flexible Information Refinement | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-297-304 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We outline a method to estimate the value of computation for a flexible
algorithm using empirical data. To determine a reasonable trade-off between
cost and value, we build an empirical model of the value obtained through
computation, and apply this model to estimate the value of computation for
quite different problems. In particular, we investigate this trade-off for the
problem of constructing policies for decision problems represented as influence
diagrams. We show how two features of our anytime algorithm provide reasonable
estimates of the value of computation in this domain.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 15:58:47 GMT"
}
] | 1,359,504,000,000 | [
[
"Horsch",
"Michael C.",
""
],
[
"Poole",
"David L.",
""
]
] |
1301.6708 | Kalev Kask | Kalev Kask, Rina Dechter | Mini-Bucket Heuristics for Improved Search | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-314-323 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The paper is a second in a series of two papers evaluating the power of a new
scheme that generates search heuristics mechanically. The heuristics are
extracted from an approximation scheme called mini-bucket elimination that was
recently introduced. The first paper introduced the idea and evaluated it
within Branch-and-Bound search. In the current paper the idea is further
extended and evaluated within Best-First search. The resulting algorithms are
compared on coding and medical diagnosis problems, using varying strength of
the mini-bucket heuristics.
Our results demonstrate an effective search scheme that permits controlled
tradeoff between preprocessing (for heuristic generation) and search.
Best-first search is shown to outperform Branch-and-Bound, when supplied with
good heuristics, and sufficient memory space.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 15:58:54 GMT"
}
] | 1,359,504,000,000 | [
[
"Kask",
"Kalev",
""
],
[
"Dechter",
"Rina",
""
]
] |
1301.6709 | Daphne Koller | Daphne Koller, Uri Lerner, Dragomir Anguelov | A General Algorithm for Approximate Inference and its Application to
Hybrid Bayes Nets | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-324-333 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The clique tree algorithm is the standard method for doing inference in
Bayesian networks. It works by manipulating clique potentials - distributions
over the variables in a clique. While this approach works well for many
networks, it is limited by the need to maintain an exact representation of the
clique potentials. This paper presents a new unified approach that combines
approximate inference and the clique tree algorithm, thereby circumventing this
limitation. Many known approximate inference algorithms can be viewed as
instances of this approach. The algorithm essentially does clique tree
propagation, using approximate inference to estimate the densities in each
clique. In many settings, the computation of the approximate clique potential
can be done easily using statistical importance sampling. Iterations are used
to gradually improve the quality of the estimation.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 15:58:59 GMT"
}
] | 1,359,504,000,000 | [
[
"Koller",
"Daphne",
""
],
[
"Lerner",
"Uri",
""
],
[
"Anguelov",
"Dragomir",
""
]
] |
1301.6712 | Ryszard Kowalczyk | Ryszard Kowalczyk | On Quantified Linguistic Approximation | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-351-358 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most fuzzy systems including fuzzy decision support and fuzzy control systems
provide out-puts in the form of fuzzy sets that represent the inferred
conclusions. Linguistic interpretation of such outputs often involves the use
of linguistic approximation that assigns a linguistic label to a fuzzy set
based on the predefined primary terms, linguistic modifiers and linguistic
connectives. More generally, linguistic approximation can be formalized in the
terms of the re-translation rules that correspond to the translation rules in
ex-plicitation (e.g. simple, modifier, composite, quantification and
qualification rules) in com-puting with words [Zadeh 1996]. However most
existing methods of linguistic approximation use the simple, modifier and
composite re-translation rules only. Although these methods can provide a
sufficient approximation of simple fuzzy sets the approximation of more complex
ones that are typical in many practical applications of fuzzy systems may be
less satisfactory. Therefore the question arises why not use in linguistic
ap-proximation also other re-translation rules corre-sponding to the
translation rules in explicitation to advantage. In particular linguistic
quantifica-tion may be desirable in situations where the conclusions
interpreted as quantified linguistic propositions can be more informative and
natu-ral. This paper presents some aspects of linguis-tic approximation in the
context of the re-translation rules and proposes an approach to linguistic
approximation with the use of quantifi-cation rules, i.e. quantified linguistic
approxima-tion. Two methods of the quantified linguistic approximation are
considered with the use of lin-guistic quantifiers based on the concepts of the
non-fuzzy and fuzzy cardinalities of fuzzy sets. A number of examples are
provided to illustrate the proposed approach.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 15:59:10 GMT"
}
] | 1,359,504,000,000 | [
[
"Kowalczyk",
"Ryszard",
""
]
] |
1301.6713 | Henry E. Kyburg Jr. | Henry E. Kyburg Jr., Choh Man Teng | Choosing Among Interpretations of Probability | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-359-365 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There is available an ever-increasing variety of procedures for managing
uncertainty. These methods are discussed in the literature of artificial
intelligence, as well as in the literature of philosophy of science. Heretofore
these methods have been evaluated by intuition, discussion, and the general
philosophical method of argument and counterexample. Almost any method of
uncertainty management will have the property that in the long run it will
deliver numbers approaching the relative frequency of the kinds of events at
issue. To find a measure that will provide a meaningful evaluation of these
treatments of uncertainty, we must look, not at the long run, but at the short
or intermediate run. Our project attempts to develop such a measure in terms of
short or intermediate length performance. We represent the effects of practical
choices by the outcomes of bets offered to agents characterized by two
uncertainty management approaches: the subjective Bayesian approach and the
Classical confidence interval approach. Experimental evaluation suggests that
the confidence interval approach can outperform the subjective approach in the
relatively short run.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 15:59:14 GMT"
}
] | 1,359,504,000,000 | [
[
"Kyburg",
"Henry E.",
"Jr."
],
[
"Teng",
"Choh Man",
""
]
] |
1301.6715 | Christopher Lusena | Christopher Lusena, Tong Li, Shelia Sittinger, Chris Wells, Judy
Goldsmith | My Brain is Full: When More Memory Helps | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-374-381 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the problem of finding good finite-horizon policies for POMDPs
under the expected reward metric. The policies considered are {em free
finite-memory policies with limited memory}; a policy is a mapping from the
space of observation-memory pairs to the space of action-memeory pairs (the
policy updates the memory as it goes), and the number of possible memory states
is a parameter of the input to the policy-finding algorithms. The algorithms
considered here are preliminary implementations of three search heuristics:
local search, simulated annealing, and genetic algorithms. We compare their
outcomes to each other and to the optimal policies for each instance. We
compare run times of each policy and of a dynamic programming algorithm for
POMDPs developed by Hansen that iteratively improves a finite-state controller
--- the previous state of the art for finite memory policies. The value of the
best policy can only improve as the amount of memory increases, up to the
amount needed for an optimal finite-memory policy. Our most surprising finding
is that more memory helps in another way: given more memory than is needed for
an optimal policy, the algorithms are more likely to converge to optimal-valued
policies.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 15:59:22 GMT"
}
] | 1,359,504,000,000 | [
[
"Lusena",
"Christopher",
""
],
[
"Li",
"Tong",
""
],
[
"Sittinger",
"Shelia",
""
],
[
"Wells",
"Chris",
""
],
[
"Goldsmith",
"Judy",
""
]
] |
1301.6716 | Anders L. Madsen | Anders L. Madsen, Finn Verner Jensen | Lazy Evaluation of Symmetric Bayesian Decision Problems | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-382-390 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Solving symmetric Bayesian decision problems is a computationally intensive
task to perform regardless of the algorithm used. In this paper we propose a
method for improving the efficiency of algorithms for solving Bayesian decision
problems. The method is based on the principle of lazy evaluation - a principle
recently shown to improve the efficiency of inference in Bayesian networks. The
basic idea is to maintain decompositions of potentials and to postpone
computations for as long as possible. The efficiency improvements obtained with
the lazy evaluation based method is emphasized through examples. Finally, the
lazy evaluation based method is compared with the hugin and valuation-based
systems architectures for solving symmetric Bayesian decision problems.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 15:59:26 GMT"
}
] | 1,359,504,000,000 | [
[
"Madsen",
"Anders L.",
""
],
[
"Jensen",
"Finn Verner",
""
]
] |
1301.6717 | Suzanne M. Mahoney | Suzanne M. Mahoney, Kathryn Blackmond Laskey | Representing and Combining Partially Specified CPTs | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-391-400 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper extends previous work with network fragments and
situation-specific network construction. We formally define the asymmetry
network, an alternative representation for a conditional probability table. We
also present an object-oriented representation for partially specified
asymmetry networks. We show that the representation is parsimonious. We define
an algebra for the elements of the representation that allows us to 'factor'
any CPT and to soundly combine the partially specified asymmetry networks.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 15:59:31 GMT"
}
] | 1,359,504,000,000 | [
[
"Mahoney",
"Suzanne M.",
""
],
[
"Laskey",
"Kathryn Blackmond",
""
]
] |
1301.6718 | Yishay Mansour | Yishay Mansour, Satinder Singh | On the Complexity of Policy Iteration | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-401-408 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Decision-making problems in uncertain or stochastic domains are often
formulated as Markov decision processes (MDPs). Policy iteration (PI) is a
popular algorithm for searching over policy-space, the size of which is
exponential in the number of states. We are interested in bounds on the
complexity of PI that do not depend on the value of the discount factor. In
this paper we prove the first such non-trivial, worst-case, upper bounds on the
number of iterations required by PI to converge to the optimal policy. Our
analysis also sheds new light on the manner in which PI progresses through the
space of policies.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 15:59:34 GMT"
}
] | 1,359,504,000,000 | [
[
"Mansour",
"Yishay",
""
],
[
"Singh",
"Satinder",
""
]
] |
1301.6719 | David A. McAllester | David A. McAllester, Satinder Singh | Approximate Planning for Factored POMDPs using Belief State
Simplification | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-409-416 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We are interested in the problem of planning for factored POMDPs. Building on
the recent results of Kearns, Mansour and Ng, we provide a planning algorithm
for factored POMDPs that exploits the accuracy-efficiency tradeoff in the
belief state simplification introduced by Boyen and Koller.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 15:59:38 GMT"
}
] | 1,359,504,000,000 | [
[
"McAllester",
"David A.",
""
],
[
"Singh",
"Satinder",
""
]
] |
1301.6720 | Nicolas Meuleau | Nicolas Meuleau, Kee-Eung Kim, Leslie Pack Kaelbling, Anthony R.
Cassandra | Solving POMDPs by Searching the Space of Finite Policies | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-417-426 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Solving partially observable Markov decision processes (POMDPs) is highly
intractable in general, at least in part because the optimal policy may be
infinitely large. In this paper, we explore the problem of finding the optimal
policy from a restricted set of policies, represented as finite state automata
of a given size. This problem is also intractable, but we show that the
complexity can be greatly reduced when the POMDP and/or policy are further
constrained. We demonstrate good empirical results with a branch-and-bound
method for finding globally optimal deterministic policies, and a
gradient-ascent method for finding locally optimal stochastic policies.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 15:59:42 GMT"
}
] | 1,359,504,000,000 | [
[
"Meuleau",
"Nicolas",
""
],
[
"Kim",
"Kee-Eung",
""
],
[
"Kaelbling",
"Leslie Pack",
""
],
[
"Cassandra",
"Anthony R.",
""
]
] |
1301.6729 | Thomas D. Nielsen | Thomas D. Nielsen, Finn Verner Jensen | Welldefined Decision Scenarios | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-502-511 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Influence diagrams serve as a powerful tool for modelling symmetric decision
problems. When solving an influence diagram we determine a set of strategies
for the decisions involved. A strategy for a decision variable is in principle
a function over its past. However, some of the past may be irrelevant for the
decision, and for computational reasons it is important not to deal with
redundant variables in the strategies. We show that current methods (e.g. the
"Decision Bayes-ball" algorithm by Shachter UAI98) do not determine the
relevant past, and we present a complete algorithm.
Actually, this paper takes a more general outset: When formulating a decision
scenario as an influence diagram, a linear temporal ordering of the decisions
variables is required. This constraint ensures that the decision scenario is
welldefined. However, the structure of a decision scenario often yields certain
decisions conditionally independent, and it is therefore unnecessary to impose
a linear temporal ordering on the decisions. In this paper we deal with partial
influence diagrams i.e. influence diagrams with only a partial temporal
ordering specified. We present a set of conditions which are necessary and
sufficient to ensure that a partial influence diagram is welldefined. These
conditions are used as a basis for the construction of an algorithm for
determining whether or not a partial influence diagram is welldefined.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 16:00:18 GMT"
}
] | 1,359,504,000,000 | [
[
"Nielsen",
"Thomas D.",
""
],
[
"Jensen",
"Finn Verner",
""
]
] |
1301.6732 | David M Pennock | David M. Pennock, Michael P. Wellman | Graphical Representations of Consensus Belief | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-531-540 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Graphical models based on conditional independence support concise encodings
of the subjective belief of a single agent. A natural question is whether the
consensus belief of a group of agents can be represented with equal parsimony.
We prove, under relatively mild assumptions, that even if everyone agrees on a
common graph topology, no method of combining beliefs can maintain that
structure. Even weaker conditions rule out local aggregation within conditional
probability tables. On a more positive note, we show that if probabilities are
combined with the logarithmic opinion pool (LogOP), then commonly held Markov
independencies are maintained. This suggests a straightforward procedure for
constructing a consensus Markov network. We describe an algorithm for computing
the LogOP with time complexity comparable to that of exact Bayesian inference.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 16:00:29 GMT"
}
] | 1,359,504,000,000 | [
[
"Pennock",
"David M.",
""
],
[
"Wellman",
"Michael P.",
""
]
] |
1301.6733 | Avi Pfeffer | Avi Pfeffer, Daphne Koller, Brian Milch, Ken T. Takusagawa | SPOOK: A System for Probabilistic Object-Oriented Knowledge
Representation | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-541-550 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In previous work, we pointed out the limitations of standard Bayesian
networks as a modeling framework for large, complex domains. We proposed a new,
richly structured modeling language, {em Object-oriented Bayesian Netorks},
that we argued would be able to deal with such domains. However, it turns out
that OOBNs are not expressive enough to model many interesting aspects of
complex domains: the existence of specific named objects, arbitrary relations
between objects, and uncertainty over domain structure. These aspects are
crucial in real-world domains such as battlefield awareness. In this paper, we
present SPOOK, an implemented system that addresses these limitations. SPOOK
implements a more expressive language that allows it to represent the
battlespace domain naturally and compactly. We present a new inference
algorithm that utilizes the model structure in a fundamental way, and show
empirically that it achieves orders of magnitude speedup over existing
approaches.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 16:00:32 GMT"
}
] | 1,359,504,000,000 | [
[
"Pfeffer",
"Avi",
""
],
[
"Koller",
"Daphne",
""
],
[
"Milch",
"Brian",
""
],
[
"Takusagawa",
"Ken T.",
""
]
] |
1301.6734 | Luigi Portinale | Luigi Portinale, Andrea Bobbio | Bayesian Networks for Dependability Analysis: an Application to Digital
Control Reliability | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-551-558 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bayesian Networks (BN) provide robust probabilistic methods of reasoning
under uncertainty, but despite their formal grounds are strictly based on the
notion of conditional dependence, not much attention has been paid so far to
their use in dependability analysis. The aim of this paper is to propose BN as
a suitable tool for dependability analysis, by challenging the formalism with
basic issues arising in dependability tasks. We will discuss how both modeling
and analysis issues can be naturally dealt with by BN. Moreover, we will show
how some limitations intrinsic to combinatorial dependability methods such as
Fault Trees can be overcome using BN. This will be pursued through the study of
a real-world example concerning the reliability analysis of a redundant digital
Programmable Logic Controller (PLC) with majority voting 2:3
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 16:00:36 GMT"
}
] | 1,359,504,000,000 | [
[
"Portinale",
"Luigi",
""
],
[
"Bobbio",
"Andrea",
""
]
] |
1301.6735 | Silja Renooij | Silja Renooij, Linda C. van der Gaag | Enhancing QPNs for Trade-off Resolution | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-559-566 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Qualitative probabilistic networks have been introduced as qualitative
abstractions of Bayesian belief networks. One of the major drawbacks of these
qualitative networks is their coarse level of detail, which may lead to
unresolved trade-offs during inference. We present an enhanced formalism for
qualitative networks with a finer level of detail. An enhanced qualitative
probabilistic network differs from a regular qualitative network in that it
distinguishes between strong and weak influences. Enhanced qualitative
probabilistic networks are purely qualitative in nature, as regular qualitative
networks are, yet allow for efficiently resolving trade-offs during inference.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 16:00:40 GMT"
}
] | 1,359,504,000,000 | [
[
"Renooij",
"Silja",
""
],
[
"van der Gaag",
"Linda C.",
""
]
] |
1301.6736 | Regis Sabbadin | Regis Sabbadin | A Possibilistic Model for Qualitative Sequential Decision Problems under
Uncertainty in Partially Observable Environments | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-567-574 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this article we propose a qualitative (ordinal) counterpart for the
Partially Observable Markov Decision Processes model (POMDP) in which the
uncertainty, as well as the preferences of the agent, are modeled by
possibility distributions. This qualitative counterpart of the POMDP model
relies on a possibilistic theory of decision under uncertainty, recently
developed. One advantage of such a qualitative framework is its ability to
escape from the classical obstacle of stochastic POMDPs, in which even with a
finite state space, the obtained belief state space of the POMDP is infinite.
Instead, in the possibilistic framework even if exponentially larger than the
state space, the belief state space remains finite.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 16:00:44 GMT"
}
] | 1,359,504,000,000 | [
[
"Sabbadin",
"Regis",
""
]
] |
1301.6739 | Ross D. Shachter | Ross D. Shachter | Efficient Value of Information Computation | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-594-601 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the most useful sensitivity analysis techniques of decision analysis
is the computation of value of information (or clairvoyance), the difference in
value obtained by changing the decisions by which some of the uncertainties are
observed. In this paper, some simple but powerful extensions to previous
algorithms are introduced which allow an efficient value of information
calculation on the rooted cluster tree (or strong junction tree) used to solve
the original decision problem.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 16:00:56 GMT"
}
] | 1,359,504,000,000 | [
[
"Shachter",
"Ross D.",
""
]
] |
1301.6740 | Hagit Shatkay | Hagit Shatkay | Learning Hidden Markov Models with Geometrical Constraints | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-602-611 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Hidden Markov models (HMMs) and partially observable Markov decision
processes (POMDPs) form a useful tool for modeling dynamical systems. They are
particularly useful for representing environments such as road networks and
office buildings, which are typical for robot navigation and planning. The work
presented here is concerned with acquiring such models. We demonstrate how
domain-specific information and constraints can be incorporated into the
statistical estimation process, greatly improving the learned models in terms
of the model quality, the number of iterations required for convergence and
robustness to reduction in the amount of available data. We present new
initialization heuristics which can be used even when the data suffers from
cumulative rotational error, new update rules for the model parameters, as an
instance of generalized EM, and a strategy for enforcing complete geometrical
consistency in the model. Experimental results demonstrate the effectiveness of
our approach for both simulated and real robot data, in traditionally
hard-to-learn environments.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 16:01:01 GMT"
}
] | 1,359,504,000,000 | [
[
"Shatkay",
"Hagit",
""
]
] |
1301.6741 | Philippe Smets | Philippe Smets | Practical Uses of Belief Functions | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-612-621 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present examples where the use of belief functions provided sound and
elegant solutions to real life problems. These are essentially characterized by
?missing' information. The examples deal with 1) discriminant analysis using a
learning set where classes are only partially known; 2) an information
retrieval systems handling inter-documents relationships; 3) the combination of
data from sensors competent on partially overlapping frames; 4) the
determination of the number of sources in a multi-sensor environment by
studying the inter-sensors contradiction. The purpose of the paper is to report
on such applications where the use of belief functions provides a convenient
tool to handle ?messy' data problems.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 16:01:05 GMT"
}
] | 1,359,504,000,000 | [
[
"Smets",
"Philippe",
""
]
] |
1301.6742 | Masami Takikawa | Masami Takikawa, Bruce D'Ambrosio | Multiplicative Factorization of Noisy-Max | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-622-630 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The noisy-or and its generalization noisy-max have been utilized to reduce
the complexity of knowledge acquisition. In this paper, we present a new
representation of noisy-max that allows for efficient inference in general
Bayesian networks. Empirical studies show that our method is capable of
computing queries in well-known large medical networks, QMR-DT and CPCS, for
which no previous exact inference method has been shown to perform well.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 16:01:09 GMT"
}
] | 1,359,504,000,000 | [
[
"Takikawa",
"Masami",
""
],
[
"D'Ambrosio",
"Bruce",
""
]
] |
1301.6744 | Volker Tresp | Volker Tresp, Michael Haft, Reimar Hofmann | Mixture Approximations to Bayesian Networks | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-639-646 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Structure and parameters in a Bayesian network uniquely specify the
probability distribution of the modeled domain. The locality of both structure
and probabilistic information are the great benefits of Bayesian networks and
require the modeler to only specify local information. On the other hand this
locality of information might prevent the modeler - and even more any other
person - from obtaining a general overview of the important relationships
within the domain. The goal of the work presented in this paper is to provide
an "alternative" view on the knowledge encoded in a Bayesian network which
might sometimes be very helpful for providing insights into the underlying
domain. The basic idea is to calculate a mixture approximation to the
probability distribution represented by the Bayesian network. The mixture
component densities can be thought of as representing typical scenarios implied
by the Bayesian model, providing intuition about the basic relationships. As an
additional benefit, performing inference in the approximate model is very
simple and intuitive and can provide additional insights. The computational
complexity for the calculation of the mixture approximations criticaly depends
on the measure which defines the distance between the probability distribution
represented by the Bayesian network and the approximate distribution. Both the
KL-divergence and the backward KL-divergence lead to inefficient algorithms.
Incidentally, the latter is used in recent work on mixtures of mean field
solutions to which the work presented here is closely related. We show,
however, that using a mean squared error cost function leads to update
equations which can be solved using the junction tree algorithm. We conclude
that the mean squared error cost function can be used for Bayesian networks in
which inference based on the junction tree is tractable. For large networks,
however, one may have to rely on mean field approximations.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 16:01:18 GMT"
}
] | 1,359,504,000,000 | [
[
"Tresp",
"Volker",
""
],
[
"Haft",
"Michael",
""
],
[
"Hofmann",
"Reimar",
""
]
] |
1301.6745 | Linda C. van der Gaag | Linda C. van der Gaag, Silja Renooij, Cilia L. M. Witteman, Berthe M.
P. Aleman, Babs G. Taal | How to Elicit Many Probabilities | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-647-654 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In building Bayesian belief networks, the elicitation of all probabilities
required can be a major obstacle. We learned the extent of this often-cited
observation in the construction of the probabilistic part of a complex
influence diagram in the field of cancer treatment. Based upon our negative
experiences with existing methods, we designed a new method for probability
elicitation from domain experts. The method combines various ideas, among which
are the ideas of transcribing probabilities and of using a scale with both
numerical and verbal anchors for marking assessments. In the construction of
the probabilistic part of our influence diagram, the method proved to allow for
the elicitation of many probabilities in little time.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 16:01:22 GMT"
}
] | 1,359,504,000,000 | [
[
"van der Gaag",
"Linda C.",
""
],
[
"Renooij",
"Silja",
""
],
[
"Witteman",
"Cilia L. M.",
""
],
[
"Aleman",
"Berthe M. P.",
""
],
[
"Taal",
"Babs G.",
""
]
] |
1301.6746 | Frans Voorbraak | Frans Voorbraak | Probabilistic Belief Change: Expansion, Conditioning and Constraining | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-655-662 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The AGM theory of belief revision has become an important paradigm for
investigating rational belief changes. Unfortunately, researchers working in
this paradigm have restricted much of their attention to rather simple
representations of belief states, namely logically closed sets of propositional
sentences. In our opinion, this has resulted in a too abstract categorisation
of belief change operations: expansion, revision, or contraction. Occasionally,
in the AGM paradigm, also probabilistic belief changes have been considered,
and it is widely accepted that the probabilistic version of expansion is
conditioning. However, we argue that it may be more correct to view
conditioning and expansion as two essentially different kinds of belief change,
and that what we call constraining is a better candidate for being considered
probabilistic expansion.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 16:01:26 GMT"
}
] | 1,359,504,000,000 | [
[
"Voorbraak",
"Frans",
""
]
] |
1301.6748 | Michael S. K. M. Wong | Michael S. K. M. Wong, C. J. Butz | Contextual Weak Independence in Bayesian Networks | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-670-679 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is well-known that the notion of (strong) conditional independence (CI) is
too restrictive to capture independencies that only hold in certain contexts.
This kind of contextual independency, called context-strong independence (CSI),
can be used to facilitate the acquisition, representation, and inference of
probabilistic knowledge. In this paper, we suggest the use of contextual weak
independence (CWI) in Bayesian networks. It should be emphasized that the
notion of CWI is a more general form of contextual independence than CSI.
Furthermore, if the contextual strong independence holds for all contexts, then
the notion of CSI becomes strong CI. On the other hand, if the weak contextual
independence holds for all contexts, then the notion of CWI becomes weak
independence (WI) nwhich is a more general noncontextual independency than
strong CI. More importantly, complete axiomatizations are studied for both the
class of WI and the class of CI and WI together. Finally, the interesting
property of WI being a necessary and sufficient condition for ensuring
consistency in granular probabilistic networks is shown.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 16:01:33 GMT"
}
] | 1,359,504,000,000 | [
[
"Wong",
"Michael S. K. M.",
""
],
[
"Butz",
"C. J.",
""
]
] |
1301.6749 | Yanping Xiang | Yanping Xiang, Finn Verner Jensen | Inference in Multiply Sectioned Bayesian Networks with Extended
Shafer-Shenoy and Lazy Propagation | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-680-687 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As Bayesian networks are applied to larger and more complex problem domains,
search for flexible modeling and more efficient inference methods is an ongoing
effort. Multiply sectioned Bayesian networks (MSBNs) extend the HUGIN inference
for Bayesian networks into a coherent framework for flexible modeling and
distributed inference.Lazy propagation extends the Shafer-Shenoy and HUGIN
inference methods with reduced space complexity. We apply the Shafer-Shenoy and
lazy propagation to inference in MSBNs. The combination of the MSBN framework
and lazy propagation provides a better framework for modeling and inference in
very large domains. It retains the modeling flexibility of MSBNs and reduces
the runtime space complexity, allowing exact inference in much larger domains
given the same computational resources.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 16:01:37 GMT"
}
] | 1,359,504,000,000 | [
[
"Xiang",
"Yanping",
""
],
[
"Jensen",
"Finn Verner",
""
]
] |
1301.6750 | Yanping Xiang | Yanping Xiang, Kim-Leng Poh | Time-Critical Dynamic Decision Making | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-688-695 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent interests in dynamic decision modeling have led to the development of
several representation and inference methods. These methods however, have
limited application under time critical conditions where a trade-off between
model quality and computational tractability is essential. This paper presents
an approach to time-critical dynamic decision modeling. A knowledge
representation and modeling method called the time-critical dynamic influence
diagram is proposed. The formalism has two forms. The condensed form is used
for modeling and model abstraction, while the deployed form which can be
converted from the condensed form is used for inference purposes. The proposed
approach has the ability to represent space-temporal abstraction within the
model. A knowledge-based meta-reasoning approach is proposed for the purpose of
selecting the best abstracted model that provide the optimal trade-off between
model quality and model tractability. An outline of the knowledge-based model
construction algorithm is also provided.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 16:01:41 GMT"
}
] | 1,359,504,000,000 | [
[
"Xiang",
"Yanping",
""
],
[
"Poh",
"Kim-Leng",
""
]
] |
1301.6751 | Nevin Lianwen Zhang | Nevin Lianwen Zhang, Stephen S. Lee, Weihong Zhang | A Method for Speeding Up Value Iteration in Partially Observable Markov
Decision Processes | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-696-703 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a technique for speeding up the convergence of value iteration for
partially observable Markov decisions processes (POMDPs). The underlying idea
is similar to that behind modified policy iteration for fully observable Markov
decision processes (MDPs). The technique can be easily incorporated into any
existing POMDP value iteration algorithms. Experiments have been conducted on
several test problems with one POMDP value iteration algorithm called
incremental pruning. We find that the technique can make incremental pruning
run several orders of magnitude faster.
| [
{
"version": "v1",
"created": "Wed, 23 Jan 2013 16:01:45 GMT"
}
] | 1,359,504,000,000 | [
[
"Zhang",
"Nevin Lianwen",
""
],
[
"Lee",
"Stephen S.",
""
],
[
"Zhang",
"Weihong",
""
]
] |
1301.6789 | D P Acharjya Ph.D | B.K.Tripathy and D.P.Acharjya | Approximation of Classification and Measures of Uncertainty in Rough Set
on Two Universal Sets | 14 pages, International Journal of Advanced Science and Technology
Vol. 40, March, 2012 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The notion of rough set captures indiscernibility of elements in a set. But,
in many real life situations, an information system establishes the relation
between different universes. This gave the extension of rough set on single
universal set to rough set on two universal sets. In this paper, we introduce
approximation of classifications and measures of uncertainty basing upon rough
set on two universal sets employing the knowledge due to binary relations.
| [
{
"version": "v1",
"created": "Fri, 25 Jan 2013 11:58:23 GMT"
}
] | 1,359,504,000,000 | [
[
"Tripathy",
"B. K.",
""
],
[
"Acharjya",
"D. P.",
""
]
] |
1301.7251 | Teresa Alsinet | Teresa Alsinet, Lluis Godo, Sandra Sandri | On the Semantics and Automated Deduction for PLFC, a Logic of
Possibilistic Uncertainty and Fuzziness | Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999) | null | null | UAI-P-1999-PG-3-12 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Possibilistic logic is a well-known graded logic of uncertainty suitable to
reason under incomplete information and partially inconsistent knowledge, which
is built upon classical first order logic. There exists for Possibilistic logic
a proof procedure based on a refutation complete resolution-style calculus.
Recently, a syntactical extension of first order Possibilistic logic (called
PLFC) dealing with fuzzy constants and fuzzily restricted quantifiers has been
proposed. Our aim is to present steps towards both the formalization of PLFC
itself and an automated deduction system for it by (i) providing a formal
semantics; (ii) defining a sound resolution-style calculus by refutation; and
(iii) describing a first-order proof procedure for PLFC clauses based on (ii)
and on a novel notion of most general substitution of two literals in a
resolution step. In contrast to standard Possibilistic logic semantics,
truth-evaluation of formulas with fuzzy constants are many-valued instead of
boolean, and consequently an extended notion of possibilistic uncertainty is
also needed.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 14:58:40 GMT"
}
] | 1,359,590,400,000 | [
[
"Alsinet",
"Teresa",
""
],
[
"Godo",
"Lluis",
""
],
[
"Sandri",
"Sandra",
""
]
] |
1301.7358 | Leila Amgoud | Leila Amgoud, Claudette Cayrol | On the Acceptability of Arguments in Preference-Based Argumentation | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-1-7 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Argumentation is a promising model for reasoning with uncertain knowledge.
The key concept of acceptability enables to differentiate arguments and
counterarguments: The certainty of a proposition can then be evaluated through
the most acceptable arguments for that proposition. In this paper, we
investigate different complementary points of view: - an acceptability based on
the existence of direct counterarguments, - an acceptability based on the
existence of defenders. Pursuing previous work on preference-based
argumentation principles, we enforce both points of view by taking into account
preference orderings for comparing arguments. Our approach is illustrated in
the context of reasoning with stratified knowldge bases.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:02:19 GMT"
}
] | 1,359,676,800,000 | [
[
"Amgoud",
"Leila",
""
],
[
"Cayrol",
"Claudette",
""
]
] |
1301.7359 | Salem Benferhat | Salem Benferhat, Claudio Sossai | Merging Uncertain Knowledge Bases in a Possibilistic Logic Framework | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-8-15 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper addresses the problem of merging uncertain information in the
framework of possibilistic logic. It presents several syntactic combination
rules to merge possibilistic knowledge bases, provided by different sources,
into a new possibilistic knowledge base. These combination rules are first
described at the meta-level outside the language of possibilistic logic. Next,
an extension of possibilistic logic, where the combination rules are inside the
language, is proposed. A proof system in a sequent form, which is sound and
complete with respect to the possibilistic logic semantics, is given.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:02:23 GMT"
}
] | 1,359,676,800,000 | [
[
"Benferhat",
"Salem",
""
],
[
"Sossai",
"Claudio",
""
]
] |
1301.7360 | Mark Bloemeke | Mark Bloemeke, Marco Valtorta | A Hybrid Algorithm to Compute Marginal and Joint Beliefs in Bayesian
Networks and Its Complexity | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-16-23 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There exist two general forms of exact algorithms for updating probabilities
in Bayesian Networks. The first approach involves using a structure, usually a
clique tree, and performing local message based calculation to extract the
belief in each variable. The second general class of algorithm involves the use
of non-serial dynamic programming techniques to extract the belief in some
desired group of variables. In this paper we present a hybrid algorithm based
on the latter approach yet possessing the ability to retrieve the belief in all
single variables. The technique is advantageous in that it saves a NP-hard
computation step over using one algorithm of each type. Furthermore, this
technique re-enforces a conjecture of Jensen and Jensen [JJ94] in that it still
requires a single NP-hard step to set up the structure on which inference is
performed, as we show by confirming Li and D'Ambrosio's [LD94] conjectured
NP-hardness of OFP.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:02:29 GMT"
}
] | 1,359,676,800,000 | [
[
"Bloemeke",
"Mark",
""
],
[
"Valtorta",
"Marco",
""
]
] |
1301.7361 | Craig Boutilier | Craig Boutilier, Ronen I. Brafman, Christopher W. Geib | Structured Reachability Analysis for Markov Decision Processes | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-24-32 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent research in decision theoretic planning has focussed on making the
solution of Markov decision processes (MDPs) more feasible. We develop a family
of algorithms for structured reachability analysis of MDPs that are suitable
when an initial state (or set of states) is known. Using compact, structured
representations of MDPs (e.g., Bayesian networks), our methods, which vary in
the tradeoff between complexity and accuracy, produce structured descriptions
of (estimated) reachable states that can be used to eliminate variables or
variable values from the problem description, reducing the size of the MDP and
making it easier to solve. One contribution of our work is the extension of
ideas from GRAPHPLAN to deal with the distributed nature of action
representations typically embodied within Bayes nets and the problem of
correlated action effects. We also demonstrate that our algorithm can be made
more complete by using k-ary constraints instead of binary constraints. Another
contribution is the illustration of how the compact representation of
reachability constraints can be exploited by several existing (exact and
approximate) abstraction algorithms for MDPs.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:02:33 GMT"
},
{
"version": "v2",
"created": "Tue, 23 Apr 2013 15:58:57 GMT"
}
] | 1,366,761,600,000 | [
[
"Boutilier",
"Craig",
""
],
[
"Brafman",
"Ronen I.",
""
],
[
"Geib",
"Christopher W.",
""
]
] |
1301.7362 | Xavier Boyen | Xavier Boyen, Daphne Koller | Tractable Inference for Complex Stochastic Processes | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-33-42 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The monitoring and control of any dynamic system depends crucially on the
ability to reason about its current status and its future trajectory. In the
case of a stochastic system, these tasks typically involve the use of a belief
state- a probability distribution over the state of the process at a given
point in time. Unfortunately, the state spaces of complex processes are very
large, making an explicit representation of a belief state intractable. Even in
dynamic Bayesian networks (DBNs), where the process itself can be represented
compactly, the representation of the belief state is intractable. We
investigate the idea of maintaining a compact approximation to the true belief
state, and analyze the conditions under which the errors due to the
approximations taken over the lifetime of the process do not accumulate to make
our answers completely irrelevant. We show that the error in a belief state
contracts exponentially as the process evolves. Thus, even with multiple
approximations, the error in our process remains bounded indefinitely. We show
how the additional structure of a DBN can be used to design our approximation
scheme, improving its performance significantly. We demonstrate the
applicability of our ideas in the context of a monitoring task, showing that
orders of magnitude faster inference can be achieved with only a small
degradation in accuracy.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:02:39 GMT"
}
] | 1,359,676,800,000 | [
[
"Boyen",
"Xavier",
""
],
[
"Koller",
"Daphne",
""
]
] |
1301.7365 | Charles Castel | Charles Castel, Corine Cossart, Catherine Tessier | Dealing with Uncertainty in Situation Assessment: towards a Symbolic
Approach | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-61-68 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The situation assessment problem is considered, in terms of object,
condition, activity, and plan recognition, based on data coming from the
real-word {em via} various sensors. It is shown that uncertainty issues are
linked both to the models and to the matching algorithm. Three different types
of uncertainties are identified, and within each one, the numerical and the
symbolic cases are distinguished. The emphasis is then put on purely symbolic
uncertainties: it is shown that they can be dealt with within a purely symbolic
framework resulting from a transposition of classical numerical estimation
tools.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:02:54 GMT"
}
] | 1,359,676,800,000 | [
[
"Castel",
"Charles",
""
],
[
"Cossart",
"Corine",
""
],
[
"Tessier",
"Catherine",
""
]
] |
1301.7366 | Enrique F. Castillo | Enrique F. Castillo, Juan Ferr\'andiz, Pilar Sanmartin | Marginalizing in Undirected Graph and Hypergraph Models | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-69-78 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Given an undirected graph G or hypergraph X model for a given set of
variables V, we introduce two marginalization operators for obtaining the
undirected graph GA or hypergraph HA associated with a given subset A c V such
that the marginal distribution of A factorizes according to GA or HA,
respectively. Finally, we illustrate the method by its application to some
practical examples. With them we show that hypergraph models allow defining a
finer factorization or performing a more precise conditional independence
analysis than undirected graph models.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:02:59 GMT"
}
] | 1,359,676,800,000 | [
[
"Castillo",
"Enrique F.",
""
],
[
"Ferrándiz",
"Juan",
""
],
[
"Sanmartin",
"Pilar",
""
]
] |
1301.7367 | Urszula Chajewska | Urszula Chajewska, Lise Getoor, Joseph Norman, Yuval Shahar | Utility Elicitation as a Classification Problem | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-79-88 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate the application of classification techniques to utility
elicitation. In a decision problem, two sets of parameters must generally be
elicited: the probabilities and the utilities. While the prior and conditional
probabilities in the model do not change from user to user, the utility models
do. Thus it is necessary to elicit a utility model separately for each new
user. Elicitation is long and tedious, particularly if the outcome space is
large and not decomposable. There are two common approaches to utility function
elicitation. The first is to base the determination of the users utility
function solely ON elicitation OF qualitative preferences.The second makes
assumptions about the form AND decomposability OF the utility function.Here we
take a different approach: we attempt TO identify the new USERs utility
function based on classification relative to a database of previously collected
utility functions. We do this by identifying clusters of utility functions that
minimize an appropriate distance measure. Having identified the clusters, we
develop a classification scheme that requires many fewer and simpler
assessments than full utility elicitation and is more robust than utility
elicitation based solely on preferences. We have tested our algorithm on a
small database of utility functions in a prenatal diagnosis domain and the
results are quite promising.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:03:05 GMT"
}
] | 1,359,676,800,000 | [
[
"Chajewska",
"Urszula",
""
],
[
"Getoor",
"Lise",
""
],
[
"Norman",
"Joseph",
""
],
[
"Shahar",
"Yuval",
""
]
] |
1301.7368 | Fabio Gagliardi Cozman | Fabio Gagliardi Cozman | Irrelevance and Independence Relations in Quasi-Bayesian Networks | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-89-96 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper analyzes irrelevance and independence relations in graphical
models associated with convex sets of probability distributions (called
Quasi-Bayesian networks). The basic question in Quasi-Bayesian networks is, How
can irrelevance/independence relations in Quasi-Bayesian networks be detected,
enforced and exploited? This paper addresses these questions through Walley's
definitions of irrelevance and independence. Novel algorithms and results are
presented for inferences with the so-called natural extensions using fractional
linear programming, and the properties of the so-called type-1 extensions are
clarified through a new generalization of d-separation.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:03:11 GMT"
}
] | 1,359,676,800,000 | [
[
"Cozman",
"Fabio Gagliardi",
""
]
] |
1301.7369 | Adnan Darwiche | Adnan Darwiche | Dynamic Jointrees | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-97-104 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is well known that one can ignore parts of a belief network when computing
answers to certain probabilistic queries. It is also well known that the
ignorable parts (if any) depend on the specific query of interest and,
therefore, may change as the query changes. Algorithms based on jointrees,
however, do not seem to take computational advantage of these facts given that
they typically construct jointrees for worst-case queries; that is, queries for
which every part of the belief network is considered relevant. To address this
limitation, we propose in this paper a method for reconfiguring jointrees
dynamically as the query changes. The reconfiguration process aims at
maintaining a jointree which corresponds to the underlying belief network after
it has been pruned given the current query. Our reconfiguration method is
marked by three characteristics: (a) it is based on a non-classical definition
of jointrees; (b) it is relatively efficient; and (c) it can reuse some of the
computations performed before a jointree is reconfigured. We present
preliminary experimental results which demonstrate significant savings over
using static jointrees when query changes are considerable.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:03:15 GMT"
}
] | 1,359,676,800,000 | [
[
"Darwiche",
"Adnan",
""
]
] |
1301.7370 | Benoit Desjardins | Benoit Desjardins | On the Semi-Markov Equivalence of Causal Models | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-105-112 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The variability of structure in a finite Markov equivalence class of causally
sufficient models represented by directed acyclic graphs has been fully
characterized. Without causal sufficiency, an infinite semi-Markov equivalence
class of models has only been characterized by the fact that each model in the
equivalence class entails the same marginal statistical dependencies. In this
paper, we study the variability of structure of causal models within a
semi-Markov equivalence class and propose a systematic approach to construct
models entailing any specific marginal statistical dependencies.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:03:20 GMT"
}
] | 1,359,676,800,000 | [
[
"Desjardins",
"Benoit",
""
]
] |
1301.7371 | Didier Dubois | Didier Dubois, Helene Fargier, Henri Prade | Comparative Uncertainty, Belief Functions and Accepted Beliefs | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-113-120 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper relates comparative belief structures and a general view of belief
management in the setting of deductively closed logical representations of
accepted beliefs. We show that the range of compatibility between the classical
deductive closure and uncertain reasoning covers precisely the nonmonotonic
'preferential' inference system of Kraus, Lehmann and Magidor and nothing else.
In terms of uncertain reasoning any possibility or necessity measure gives
birth to a structure of accepted beliefs. The classes of probability functions
and of Shafer's belief functions which yield belief sets prove to be very
special ones.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:03:26 GMT"
}
] | 1,359,676,800,000 | [
[
"Dubois",
"Didier",
""
],
[
"Fargier",
"Helene",
""
],
[
"Prade",
"Henri",
""
]
] |
1301.7372 | Didier Dubois | Didier Dubois, Henri Prade, Regis Sabbadin | Qualitative Decision Theory with Sugeno Integrals | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-121-128 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents an axiomatic framework for qualitative decision under
uncertainty in a finite setting. The corresponding utility is expressed by a
sup-min expression, called Sugeno (or fuzzy) integral. Technically speaking,
Sugeno integral is a median, which is indeed a qualitative counterpart to the
averaging operation underlying expected utility. The axiomatic justification of
Sugeno integral-based utility is expressed in terms of preference between acts
as in Savage decision theory. Pessimistic and optimistic qualitative utilities,
based on necessity and possibility measures, previously introduced by two of
the authors, can be retrieved in this setting by adding appropriate axioms.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:03:31 GMT"
}
] | 1,359,676,800,000 | [
[
"Dubois",
"Didier",
""
],
[
"Prade",
"Henri",
""
],
[
"Sabbadin",
"Regis",
""
]
] |
1301.7379 | Vu A. Ha | Vu A. Ha, Peter Haddawy | Towards Case-Based Preference Elicitation: Similarity Measures on
Preference Structures | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-193-201 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While decision theory provides an appealing normative framework for
representing rich preference structures, eliciting utility or value functions
typically incurs a large cost. For many applications involving interactive
systems this overhead precludes the use of formal decision-theoretic models of
preference. Instead of performing elicitation in a vacuum, it would be useful
if we could augment directly elicited preferences with some appropriate default
information. In this paper we propose a case-based approach to alleviating the
preference elicitation bottleneck. Assuming the existence of a population of
users from whom we have elicited complete or incomplete preference structures,
we propose eliciting the preferences of a new user interactively and
incrementally, using the closest existing preference structures as potential
defaults. Since a notion of closeness demands a measure of distance among
preference structures, this paper takes the first step of studying various
distance measures over fully and partially specified preference structures. We
explore the use of Euclidean distance, Spearmans footrule, and define a new
measure, the probabilistic distance. We provide computational techniques for
all three measures.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:04:06 GMT"
}
] | 1,359,676,800,000 | [
[
"Ha",
"Vu A.",
""
],
[
"Haddawy",
"Peter",
""
]
] |
1301.7380 | Eric A. Hansen | Eric A. Hansen | Solving POMDPs by Searching in Policy Space | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-211-219 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most algorithms for solving POMDPs iteratively improve a value function that
implicitly represents a policy and are said to search in value function space.
This paper presents an approach to solving POMDPs that represents a policy
explicitly as a finite-state controller and iteratively improves the controller
by search in policy space. Two related algorithms illustrate this approach. The
first is a policy iteration algorithm that can outperform value iteration in
solving infinitehorizon POMDPs. It provides the foundation for a new heuristic
search algorithm that promises further speedup by focusing computational effort
on regions of the problem space that are reachable, or likely to be reached,
from a start state.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:04:11 GMT"
}
] | 1,359,676,800,000 | [
[
"Hansen",
"Eric A.",
""
]
] |
1301.7381 | Milos Hauskrecht | Milos Hauskrecht, Nicolas Meuleau, Leslie Pack Kaelbling, Thomas L.
Dean, Craig Boutilier | Hierarchical Solution of Markov Decision Processes using Macro-actions | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-220-229 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate the use of temporally abstract actions, or macro-actions, in
the solution of Markov decision processes. Unlike current models that combine
both primitive actions and macro-actions and leave the state space unchanged,
we propose a hierarchical model (using an abstract MDP) that works with
macro-actions only, and that significantly reduces the size of the state space.
This is achieved by treating macroactions as local policies that act in certain
regions of state space, and by restricting states in the abstract MDP to those
at the boundaries of regions. The abstract MDP approximates the original and
can be solved more efficiently. We discuss several ways in which macro-actions
can be generated to ensure good solution quality. Finally, we consider ways in
which macro-actions can be reused to solve multiple, related MDPs; and we show
that this can justify the computational overhead of macro-action generation.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:04:16 GMT"
}
] | 1,359,676,800,000 | [
[
"Hauskrecht",
"Milos",
""
],
[
"Meuleau",
"Nicolas",
""
],
[
"Kaelbling",
"Leslie Pack",
""
],
[
"Dean",
"Thomas L.",
""
],
[
"Boutilier",
"Craig",
""
]
] |
1301.7383 | Holger H. Hoos | Holger H. Hoos, Thomas Stutzle | Evaluating Las Vegas Algorithms - Pitfalls and Remedies | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-238-245 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Stochastic search algorithms are among the most sucessful approaches for
solving hard combinatorial problems. A large class of stochastic search
approaches can be cast into the framework of Las Vegas Algorithms (LVAs). As
the run-time behavior of LVAs is characterized by random variables, the
detailed knowledge of run-time distributions provides important information for
the analysis of these algorithms. In this paper we propose a novel methodology
for evaluating the performance of LVAs, based on the identification of
empirical run-time distributions. We exemplify our approach by applying it to
Stochastic Local Search (SLS) algorithms for the satisfiability problem (SAT)
in propositional logic. We point out pitfalls arising from the use of improper
empirical methods and discuss the benefits of the proposed methodology for
evaluating and comparing LVAs.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:04:26 GMT"
}
] | 1,359,676,800,000 | [
[
"Hoos",
"Holger H.",
""
],
[
"Stutzle",
"Thomas",
""
]
] |
1301.7384 | Michael C. Horsch | Michael C. Horsch, David L. Poole | An Anytime Algorithm for Decision Making under Uncertainty | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-246-255 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present an anytime algorithm which computes policies for decision problems
represented as multi-stage influence diagrams. Our algorithm constructs
policies incrementally, starting from a policy which makes no use of the
available information. The incremental process constructs policies which
includes more of the information available to the decision maker at each step.
While the process converges to the optimal policy, our approach is designed for
situations in which computing the optimal policy is infeasible. We provide
examples of the process on several large decision problems, showing that, for
these examples, the process constructs valuable (but sub-optimal) policies
before the optimal policy would be available by traditional methods.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:04:31 GMT"
}
] | 1,359,676,800,000 | [
[
"Horsch",
"Michael C.",
""
],
[
"Poole",
"David L.",
""
]
] |
1301.7386 | Pablo H. Ibarguengoytia | Pablo H. Ibarguengoytia, Luis Enrique Sucar, Sunil Vadera | Any Time Probabilistic Reasoning for Sensor Validation | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-266-273 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For many real time applications, it is important to validate the information
received from the sensors before entering higher levels of reasoning. This
paper presents an any time probabilistic algorithm for validating the
information provided by sensors. The system consists of two Bayesian network
models. The first one is a model of the dependencies between sensors and it is
used to validate each sensor. It provides a list of potentially faulty sensors.
To isolate the real faults, a second Bayesian network is used, which relates
the potential faults with the real faults. This second model is also used to
make the validation algorithm any time, by validating first the sensors that
provide more information. To select the next sensor to validate, and measure
the quality of the results at each stage, an entropy function is used. This
function captures in a single quantity both the certainty and specificity
measures of any time algorithms. Together, both models constitute a mechanism
for validating sensors in an any time fashion, providing at each step the
probability of correct/faulty for each sensor, and the total quality of the
results. The algorithm has been tested in the validation of temperature sensors
of a power plant.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:04:41 GMT"
}
] | 1,359,676,800,000 | [
[
"Ibarguengoytia",
"Pablo H.",
""
],
[
"Sucar",
"Luis Enrique",
""
],
[
"Vadera",
"Sunil",
""
]
] |
1301.7387 | Manfred Jaeger | Manfred Jaeger | Measure Selection: Notions of Rationality and Representation
Independence | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-274-281 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We take another look at the general problem of selecting a preferred
probability measure among those that comply with some given constraints. The
dominant role that entropy maximization has obtained in this context is
questioned by arguing that the minimum information principle on which it is
based could be supplanted by an at least as plausible "likelihood of evidence"
principle. We then review a method for turning given selection functions into
representation independent variants, and discuss the tradeoffs involved in this
transformation.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:04:45 GMT"
}
] | 1,359,676,800,000 | [
[
"Jaeger",
"Manfred",
""
]
] |
1301.7391 | Michael Kearns | Michael Kearns, Yishay Mansour | Exact Inference of Hidden Structure from Sample Data in Noisy-OR
Networks | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-304-310 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the literature on graphical models, there has been increased attention
paid to the problems of learning hidden structure (see Heckerman [H96] for
survey) and causal mechanisms from sample data [H96, P88, S93, P95, F98]. In
most settings we should expect the former to be difficult, and the latter
potentially impossible without experimental intervention. In this work, we
examine some restricted settings in which perfectly reconstruct the hidden
structure solely on the basis of observed sample data.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:05:04 GMT"
}
] | 1,359,676,800,000 | [
[
"Kearns",
"Michael",
""
],
[
"Mansour",
"Yishay",
""
]
] |
1301.7394 | Vasilica Lepar | Vasilica Lepar, Prakash P. Shenoy | A Comparison of Lauritzen-Spiegelhalter, Hugin, and Shenoy-Shafer
Architectures for Computing Marginals of Probability Distributions | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-328-337 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the last decade, several architectures have been proposed for exact
computation of marginals using local computation. In this paper, we compare
three architectures - Lauritzen-Spiegelhalter, Hugin, and Shenoy-Shafer - from
the perspective of graphical structure for message propagation, message-passing
scheme, computational efficiency, and storage efficiency.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:05:20 GMT"
}
] | 1,359,676,800,000 | [
[
"Lepar",
"Vasilica",
""
],
[
"Shenoy",
"Prakash P.",
""
]
] |
1301.7395 | Chao-Lin Liu | Chao-Lin Liu, Michael P. Wellman | Incremental Tradeoff Resolution in Qualitative Probabilistic Networks | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-338-345 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Qualitative probabilistic reasoning in a Bayesian network often reveals
tradeoffs: relationships that are ambiguous due to competing qualitative
influences. We present two techniques that combine qualitative and numeric
probabilistic reasoning to resolve such tradeoffs, inferring the qualitative
relationship between nodes in a Bayesian network. The first approach
incrementally marginalizes nodes that contribute to the ambiguous qualitative
relationships. The second approach evaluates approximate Bayesian networks for
bounds of probability distributions, and uses these bounds to determinate
qualitative relationships in question. This approach is also incremental in
that the algorithm refines the state spaces of random variables for tighter
bounds until the qualitative relationships are resolved. Both approaches
provide systematic methods for tradeoff resolution at potentially lower
computational cost than application of purely numeric methods.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:05:25 GMT"
}
] | 1,359,676,800,000 | [
[
"Liu",
"Chao-Lin",
""
],
[
"Wellman",
"Michael P.",
""
]
] |
1301.7396 | Chao-Lin Liu | Chao-Lin Liu, Michael P. Wellman | Using Qualitative Relationships for Bounding Probability Distributions | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-346-353 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We exploit qualitative probabilistic relationships among variables for
computing bounds of conditional probability distributions of interest in
Bayesian networks. Using the signs of qualitative relationships, we can
implement abstraction operations that are guaranteed to bound the distributions
of interest in the desired direction. By evaluating incrementally improved
approximate networks, our algorithm obtains monotonically tightening bounds
that converge to exact distributions. For supermodular utility functions, the
tightening bounds monotonically reduce the set of admissible decision
alternatives as well.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:05:30 GMT"
}
] | 1,359,676,800,000 | [
[
"Liu",
"Chao-Lin",
""
],
[
"Wellman",
"Michael P.",
""
]
] |
1301.7397 | Thomas Lukasiewicz | Thomas Lukasiewicz | Magic Inference Rules for Probabilistic Deduction under Taxonomic
Knowledge | Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998) | null | null | UAI-P-1998-PG-354-361 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present locally complete inference rules for probabilistic deduction from
taxonomic and probabilistic knowledge-bases over conjunctive events. Crucially,
in contrast to similar inference rules in the literature, our inference rules
are locally complete for conjunctive events and under additional taxonomic
knowledge. We discover that our inference rules are extremely complex and that
it is at first glance not clear at all where the deduced tightest bounds come
from. Moreover, analyzing the global completeness of our inference rules, we
find examples of globally very incomplete probabilistic deductions. More
generally, we even show that all systems of inference rules for taxonomic and
probabilistic knowledge-bases over conjunctive events are globally incomplete.
We conclude that probabilistic deduction by the iterative application of
inference rules on interval restrictions for conditional probabilities, even
though considered very promising in the literature so far, seems very limited
in its field of application.
| [
{
"version": "v1",
"created": "Wed, 30 Jan 2013 15:05:34 GMT"
}
] | 1,359,676,800,000 | [
[
"Lukasiewicz",
"Thomas",
""
]
] |
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