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arXiv · 1109.2153

mGPT: A Probabilistic Planner Based on Heuristic Search

B. Bonet·H. Geffner
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Abstract

We describe the version of the GPT planner used in the probabilistic track of the 4th International Planning Competition (IPC-4). This version, called mGPT, solves Markov Decision Processes specified in the PPDDL language by extracting and using different classes of lower bounds along with various heuristic-search algorithms. The lower bounds are extracted from deterministic relaxations where the alternative probabilistic effects of an action are mapped into different, independent, deterministic actions. The heuristic-search algorithms use these lower bounds for focusing the updates and delivering a consistent value function over all states reachable from the initial state and the greedy policy.

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B. Bonet, H. Geffner. 2011-09-09. mGPT: A Probabilistic Planner Based on Heuristic Search. https://doi.org/10.1613/jair.1688

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