Intelligent Belief State Sampling for Conformant Planning
Abstract
We propose a new method for conformant planning based on two ideas. First given a small sample of the initial belief state we reduce conformant planning for this sample to a classical planning problem, giving us a candidate solution. Second we exploit regression as a way to compactly represent necessary conditions for such a solution to be valid for the non-deterministic setting. If necessary, we use the resulting formula to extract a counter-example to populate our next sampling. Our experiments show that this approach is competitive on a class of problems that are hard for traditional planners, and also returns generally shorter plans. We are also able to demonstrate unsatisfiability of some problems.
Cite
Text
Grastien and Scala. "Intelligent Belief State Sampling for Conformant Planning." International Joint Conference on Artificial Intelligence, 2017. doi:10.24963/IJCAI.2017/603Markdown
[Grastien and Scala. "Intelligent Belief State Sampling for Conformant Planning." International Joint Conference on Artificial Intelligence, 2017.](https://mlanthology.org/ijcai/2017/grastien2017ijcai-intelligent/) doi:10.24963/IJCAI.2017/603BibTeX
@inproceedings{grastien2017ijcai-intelligent,
title = {{Intelligent Belief State Sampling for Conformant Planning}},
author = {Grastien, Alban and Scala, Enrico},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2017},
pages = {4317-4323},
doi = {10.24963/IJCAI.2017/603},
url = {https://mlanthology.org/ijcai/2017/grastien2017ijcai-intelligent/}
}