Strong Probabilistic Planning

Authors:
Silvio Lago Pereira;Leliane Nunes Barros;Fábio Gagliardi Cozman
Affiliations:
Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil 1010;Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil 1010;Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil 1010
Venue:
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Year:
2008

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Abstract

We consider the problem of synthesizing policies, in domains where actions have probabilisticeffects, that are optimal in the expected-caseamong the optimal worst-case strongpolicies. Thus we combine features from nondeterministic and probabilistic planning in a single framework. We present an algorithm that combines dynamic programming and model checking techniques to find plans satisfying the problem requirements: the strong preimage computation from model checking is used to avoid actions that lead to cycles or dead ends, reducing the model to a Markov Decision Process where all possible policies are strong and worst-case optimal (i.e., successful and minimum length with probability 1). We show that backward induction can then be used to select a policy in this reduced model. The resulting algorithm is presented in two versions (enumerative and symbolic); we show that the latter version allows planning with extended reachability goals.