On the Effectiveness of Belief State Representation in Contingent Planning
Abstract
This work proposes new approaches to contingent planning using alternative belief state representations extended from those in conformant planning and a new AND/OR forward search algorithm, called PrAO, for contingent solutions. Each representation was implemented in a new contingent planner. The important role of belief state representation has been confirmed by the fact that our planners all outperform other stateof- the-art planners on most benchmarks and the comparison of their performances varies across all the benchmarks even using the same search algorithm PrAO and same unsophisticated heuristic scheme. The work identifies the properties of each representation method that affect the performance.
Cite
Text
To et al. "On the Effectiveness of Belief State Representation in Contingent Planning." AAAI Conference on Artificial Intelligence, 2011. doi:10.1609/AAAI.V25I1.8068Markdown
[To et al. "On the Effectiveness of Belief State Representation in Contingent Planning." AAAI Conference on Artificial Intelligence, 2011.](https://mlanthology.org/aaai/2011/to2011aaai-effectiveness/) doi:10.1609/AAAI.V25I1.8068BibTeX
@inproceedings{to2011aaai-effectiveness,
title = {{On the Effectiveness of Belief State Representation in Contingent Planning}},
author = {To, Son Thanh and Son, Tran Cao and Pontelli, Enrico},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2011},
pages = {1818-1819},
doi = {10.1609/AAAI.V25I1.8068},
url = {https://mlanthology.org/aaai/2011/to2011aaai-effectiveness/}
}