mGPT: A Probabilistic Planner Based on Heuristic Search
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.
Cite
Text
Bonet and Geffner. "mGPT: A Probabilistic Planner Based on Heuristic Search." Journal of Artificial Intelligence Research, 2005. doi:10.1613/JAIR.1688Markdown
[Bonet and Geffner. "mGPT: A Probabilistic Planner Based on Heuristic Search." Journal of Artificial Intelligence Research, 2005.](https://mlanthology.org/jair/2005/bonet2005jair-mgpt/) doi:10.1613/JAIR.1688BibTeX
@article{bonet2005jair-mgpt,
title = {{mGPT: A Probabilistic Planner Based on Heuristic Search}},
author = {Bonet, Blai and Geffner, Hector},
journal = {Journal of Artificial Intelligence Research},
year = {2005},
pages = {933-944},
doi = {10.1613/JAIR.1688},
volume = {24},
url = {https://mlanthology.org/jair/2005/bonet2005jair-mgpt/}
}