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    UAI
    2003
    87views Artificial Intelligence» more  UAI 2003»
    Implementation and Comparison of Solution Methods for Decision Processes with Non-Markovian Rewards
    13 years 11 months ago
    Implementation and Comparison of Solution Methods for Decision Processes with Non-Markovian Rewards
    Download  users.cecs.anu.edu.au
    This paper examines a number of solution methods for decision processes with non-Markovian rewards (NMRDPs). They all exploit a temporal logic specification of the reward functio...
    Charles Gretton, David Price, Sylvie Thiéba...
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    UAI
    2003
    122views Artificial Intelligence» more  UAI 2003»
    Monte-Carlo optimizations for resource allocation problems in stochastic network systems
    13 years 11 months ago
    Monte-Carlo optimizations for resource allocation problems in stochastic network systems
    Download  www.cs.pitt.edu
    Real-world distributed systems and networks are often unreliable and subject to random failures of its components. Such a stochastic behavior affects adversely the complexity of o...
    Milos Hauskrecht, Tomás Singliar
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    UAI
    2003
    174views Artificial Intelligence» more  UAI 2003»
    Approximate Inference and Constrained Optimization
    13 years 11 months ago
    Approximate Inference and Constrained Optimization
    Download  www.snn.ru.nl
    Loopy and generalized belief propagation are popular algorithms for approximate inference in Markov random fields and Bayesian networks. Fixed points of these algorithms correspo...
    Tom Heskes, Kees Albers, Bert Kappen
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    UAI
    2003
    99views Artificial Intelligence» more  UAI 2003»
    Systematic vs. Non-systematic Algorithms for Solving the MPE Task
    13 years 11 months ago
    Systematic vs. Non-systematic Algorithms for Solving the MPE Task
    Download  www.ics.uci.edu
    The paper explores the power of two systematic Branch and Bound search algorithms that exploit partition-based heuristics, BBBT (a new algorithm for which the heuristic informatio...
    Radu Marinescu 0002, Kalev Kask, Rina Dechter
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    UAI
    2003
    154views Artificial Intelligence» more  UAI 2003»
    Learning Continuous Time Bayesian Networks
    13 years 11 months ago
    Learning Continuous Time Bayesian Networks
    Download  robotics.stanford.edu
    Continuous time Bayesian networks (CTBN) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cycli...
    Uri Nodelman, Christian R. Shelton, Daphne Koller
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