Conditional planning in the discrete belief space

Authors:
Jussi Rintanen
Affiliations:
Albert-Ludwigs-Universität Freiburg, Institut für Informatik, Freiburg im Breisgau, Germany
Venue:
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Year:
2005

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Abstract

Probabilistic planning with observability restrictions, as formalized for example as partially observable Markov decision processes (POMDP), has a wide range of applications, but it is computationally extremely difficult. For POMDPs, the most general decision problems about existence of policies satisfying certain properties are undecidable. We consider a computationally easier form of planning that ignores exact probabilities, and give an algorithm for a class of planning problems with partial observability. We show that the basic backup step in the algorithm is NP-complete. Then we proceed to give an algorithm for the backup step, and demonstrate how it can be used as a basis of an efficient algorithm for constructing plans.