Article: mGPT: A Probabilistic Planner Based on Heuristic Search

[jai...@ptolemy.arc.nasa.gov]

JAIR is pleased to announce the publication of the following article:

Bonet, B. and Geffner, H. (2005)
"mGPT: A Probabilistic Planner Based on Heuristic Search",
Volume 24, pages 933-944.

For quick access via your WWW browser, use this URL:
http://www.jair.org/abstracts/bonet05a.html

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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Steven Minton
JAIR Managing Editor