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    UAI
    1998
    75views Artificial Intelligence» more  UAI 1998»
    Incremental Tradeoff Resolution in Qualitative Probabilistic Networks
    13 years 6 months ago
    Incremental Tradeoff Resolution in Qualitative Probabilistic Networks
    Download  www.aaai.org
    Qualitativeprobabilistic reasoningin a Bayesiannetworkoften reveals tradeoffs: relationships that are ambiguousdue to competingqualitative influences. Wepresent twotechniquesthat ...
    Chao-Lin Liu, Michael P. Wellman
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    UAI
    1998
    84views Artificial Intelligence» more  UAI 1998»
    Measure Selection: Notions of Rationality and Representation Independence
    13 years 6 months ago
    Measure Selection: Notions of Rationality and Representation Independence
    Download  folli.loria.fr
    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 maximi...
    Manfred Jaeger
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    UAI
    1998
    106views Artificial Intelligence» more  UAI 1998»
    Any Time Probabilistic Reasoning for Sensor Validation
    13 years 6 months ago
    Any Time Probabilistic Reasoning for Sensor Validation
    Download  usir.salford.ac.uk
    Pablo H. Ibargüengoytia, Luis Enrique Sucar, ...
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    UAI
    1998
    109views Artificial Intelligence» more  UAI 1998»
    An Anytime Algorithm for Decision Making under Uncertainty
    13 years 6 months ago
    An Anytime Algorithm for Decision Making under Uncertainty
    Download  www.cs.ubc.ca
    We present an anytime algorithm which computes policies for decision problems represented as multi-stage influence diagrams. Our algorithm constructs policies incrementally, start...
    Michael C. Horsch, David Poole
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    UAI
    1998
    123views Artificial Intelligence» more  UAI 1998»
    Large Deviation Methods for Approximate Probabilistic Inference
    13 years 6 months ago
    Large Deviation Methods for Approximate Probabilistic Inference
    Download  www.cis.upenn.edu
    We study two-layer belief networks of binary random variables in which the conditional probabilities Pr childjparents depend monotonically on weighted sums of the parents. In larg...
    Michael J. Kearns, Lawrence K. Saul
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    UAI
    1998
    92views Artificial Intelligence» more  UAI 1998»
    Exact Inference of Hidden Structure from Sample Data in noisy-OR Networks
    13 years 6 months ago
    Exact Inference of Hidden Structure from Sample Data in noisy-OR Networks
    Download  www.cis.upenn.edu
    Michael J. Kearns, Yishay Mansour
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    UAI
    1998
    96views Artificial Intelligence» more  UAI 1998»
    Inferring Informational Goals from Free-Text Queries: A Bayesian Approach
    13 years 6 months ago
    Inferring Informational Goals from Free-Text Queries: A Bayesian Approach
    Download  research.microsoft.com
    People using consumer software applications typically do not use technical jargon when querying an online database of help topics. Rather, they attempt to communicate their goals ...
    David Heckerman, Eric Horvitz
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    UAI
    1998
    126views Artificial Intelligence» more  UAI 1998»
    Axiomatizing Causal Reasoning
    13 years 6 months ago
    Axiomatizing Causal Reasoning
    Download  www.jair.org
    Causal models defined in terms of a collection of equations, as defined by Pearl, are axiomatized here. Axiomatizations are provided for three successively more general classes of...
    Joseph Y. Halpern
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    UAI
    1998
    91views Artificial Intelligence» more  UAI 1998»
    Hierarchical Solution of Markov Decision Processes using Macro-actions
    13 years 6 months ago
    Hierarchical Solution of Markov Decision Processes using Macro-actions
    Download  www.cs.toronto.edu
    tigate 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-...
    Milos Hauskrecht, Nicolas Meuleau, Leslie Pack Kae...
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    UAI
    1998
    168views Artificial Intelligence» more  UAI 1998»
    Learning the Structure of Dynamic Probabilistic Networks
    13 years 6 months ago
    Learning the Structure of Dynamic Probabilistic Networks
    Download  www.cs.ubc.ca
    Dynamic probabilistic networks are a compact representation of complex stochastic processes. In this paper we examine how to learn the structure of a DPN from data. We extend stru...
    Nir Friedman, Kevin P. Murphy, Stuart J. Russell
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