Extending PDDL to Model Stochastic Decision Processes

Håkan L. S. Younes

Abstract
We present an extension of PDDL for modeling stochastic decision processes. Our domain description language allows the specification of actions with probabilistic effects, exogenous events, and actions and events with delayed effects. The result is a language that can be used to specify stochastic decision processes, both discrete-time and continuous-time, of varying complexity. We also propose the use of established logic formalisms, taken from the model checking community, for specifying probabilistic temporally extended goals.

Full paper: PDF, PS (9 pages, 36 references)

Presentation: PDF (33 slides)

Citings

  1. Alan Fern, Sungwook Yoon, and Robert Givan. 2006. Approximate policy iteration with a policy language bias: Solving relational Markov decision processes. Journal of Artificial Intelligence Research 25:75–118.

  2. Luís Macedo and Amílcar Cardoso. 2004. Case-based, decision-theoretic, HTN planning. In Proceedings of the 7th European Conference on Advances in Case-Based Reasoning, 257–271. Springer.


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