Probabilistic Planning via Heuristic Forward Search and Weighted Model Counting
Abstract
We present a new algorithm for probabilistic planning with no observability. Our algorithm, called Probabilistic-FF, extends the heuristic forward-search machinery of Conformant-FF to problems with probabilistic uncertainty about both the initial state and action effects. Specifically, Probabilistic-FF combines Conformant-FFs techniques with a powerful machinery for weighted model counting in (weighted) CNFs, serving to elegantly define both the search space and the heuristic function. Our evaluation of Probabilistic-FF shows its fine scalability in a range of probabilistic domains, constituting a several orders of magnitude improvement over previous results in this area. We use a problematic case to point out the main open issue to be addressed by further research.
Cite
@article{arxiv.1111.0044,
title = {Probabilistic Planning via Heuristic Forward Search and Weighted Model Counting},
author = {C. Domshlak and J. Hoffmann},
journal= {arXiv preprint arXiv:1111.0044},
year = {2011}
}