Stochastic dynamic programming with factored representations

Markov decisionprocesses(MDPs) haveproven to be popular models for decision-theoretic planning, but standard dynamic programming algorithms for solving MDPs rely on explicit, state-based 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 decision-tree representation of rewards. Based on this representation, we develop versions of standard dynamic programming algorithms that directly manipulate decision-tree representations of policies and value functions. This generally obviates the need for state-by-state computation, aggregating states at the leaves of these trees and requiring computations only for each aggregate state. The key to...

• Type: Journal
• Year: 2000
• Where: AI
• Authors: Craig Boutilier, Richard Dearden, Moisés Goldszmidt
• Keywords: AI 2000, Artificial Intelligence, Decision-tree Representations, Dynamic Programming, Dynamic Programming Algorithms