Correlated Action Effects in Decision Theoretic Regression

Abstract

Much recent research in decision theoretic planning has adopted Markov decision processes (MDPs) as the model of choice, and has attempted to make their solution more tractable by exploiting problem structure. One particular algorithm, structured policy construction achieves this by means of a decision theoretic analog of goal regression using action descriptions based on Bayesian networks with tree-structured conditional probability tables. The algorithm as presented is not able to deal with actions with correlated effects. We describe a new decision theoretic regression operator that corrects this weakness. While conceptually straightforward, this extension requires a somewhat more complicated technical approach.

Appears in Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI1997)

References in corpus (1)

• Context-Specific Independence in Bayesian Networks

Cited by in corpus (5)

• Decision-Theoretic Planning: Structural Assumptions and Computational Leverage
• SPUDD: Stochastic Planning using Decision Diagrams
• Structured Reachability Analysis for Markov Decision Processes
• Proximity-Based Non-uniform Abstractions for Approximate Planning
• CAMPs: Learning Context-Specific Abstractions for Efficient Planning in Factored MDPs