Unifying nondeterministic and probabilistic planning through imprecise markov decision processes

Authors:
Felipe W. Trevizan;Fábio G. Cozman;Leliane N. de Barros
Affiliations:
Instituto de Matemática e Estatística, Universidade de São Paulo, São Paulo, SP, Brazil;Escola Politécnica, Universidade de São Paulo, São Paulo, SP, Brazil;Instituto de Matemática e Estatística, Universidade de São Paulo, São Paulo, SP, Brazil
Venue:
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
Year:
2006

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Abstract

This paper proposes an unifying formulation for nondeterministic and probabilistic planning. These two strands of AI planning have followed different strategies: while nondeterministic planning usually looks for minimax (or worst-case) policies, probabilistic planning attempts to maximize expected reward. In this paper we show that both problems are special cases of a more general approach, and we demonstrate that the resulting structures are Markov Decision Processes with Imprecise Probabilities (MDPIPs). We also show how existing algorithms for MDPIPs can be adapted to planning under uncertainty.