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ContextSpecific Independence in Bayesian Networks
, 1996
"... Bayesiannetworks provide a languagefor qualitatively representing the conditional independence properties of a distribution. This allows a natural and compact representation of the distribution, eases knowledge acquisition, and supports effective inference algorithms. ..."
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Cited by 341 (28 self)
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Bayesiannetworks provide a languagefor qualitatively representing the conditional independence properties of a distribution. This allows a natural and compact representation of the distribution, eases knowledge acquisition, and supports effective inference algorithms.
Stochastic Dynamic Programming with Factored Representations
, 1997
"... Markov decision processes(MDPs) have proven to be popular models for decisiontheoretic planning, but standard dynamic programming algorithms for solving MDPs rely on explicit, statebased specifications and computations. To alleviate the combinatorial problems associated with such methods, we propo ..."
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Cited by 190 (10 self)
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Markov decision processes(MDPs) have proven to be popular models for decisiontheoretic planning, but standard dynamic programming algorithms for solving MDPs rely on explicit, statebased specifications and computations. To alleviate the combinatorial problems associated with such methods, we propose new representational and computational techniques for MDPs that exploit certain types of problem structure. We use dynamic Bayesian networks (with decision trees representing the local families of conditional probability distributions) to represent stochastic actions in an MDP, together with a decisiontree representation of rewards. Based on this representation, we develop versions of standard dynamic programming algorithms that directly manipulate decisiontree representations of policies and value functions. This generally obviates the need for statebystate computation, aggregating states at the leaves of these trees and requiring computations only for each aggregate state. The key to these algorithms is a decisiontheoretic generalization of classic regression analysis, in which we determine the features relevant to predicting expected value. We demonstrate the method empirically on several planning problems,
Approximating value trees in structured dynamic programming
, 1996
"... We propose and examine a method of approximate dynamic programming for Markov decision processes based on structured problem representations. We assume an MDP is represented using a dynamic Bayesian network, and construct value functions using decision trees as our function representation. The size ..."
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Cited by 42 (14 self)
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We propose and examine a method of approximate dynamic programming for Markov decision processes based on structured problem representations. We assume an MDP is represented using a dynamic Bayesian network, and construct value functions using decision trees as our function representation. The size of the representation is kept within acceptable limits by pruning these value trees so that leaves represent possible ranges of values, thus approximating the value functions produced during optimization. We propose a method for detecting convergence,prove errors bounds on the resulting approximately optimal value functions and policies, and describe some preliminary experimental results. 1