PPDDL1.0: An Extension to PDDL for Expressing Planning Domains with Probabilistic Effects

Håkan L. S. Younes Michael L. Littman

Abstract
We desribe a variation of the planning domain definition language, PDDL, that permits the modeling of probabilistic planning problems with rewards. This language, PPDDL1.0, was used as the input language for the probabilistic track of the 4th International Planning Competition. We provide the complete syntax for PPDDL1.0 and give a semantics of PPDDL1.0 planning problems in terms of Markov decision processes.

Full paper: PDF, PS (23 pages, 19 references)

Citings

  1. Hanna M. Pasula, Luke S. Zettlemoyer, and Leslie Pack Kaelbling. 2007. Learning symbolic models of stochastic domains. Journal of Artificial Intelligence Research 29:309–352.

  2. Sylvie Thiébaux, Charles Gretton, John Slaney, David Price, and Froduald Kabanza. 2006. Decision-theoretic planning with non-Markovian rewards. Journal of Artificial Intelligence Research 25:17–74.

  3. Daniel Bryce, Subbarao Kambhampati, and David E. Smith. 2006. Sequential Monte Carlo in probabilistic planning reachability heuristics. In Proceedings of the Sixteenth International Conference on Automated Planning and Scheduling, 233–242. AAAI Press.


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