Probabilistic Planning via Heuristic Forward Search and Weighted Model Counting

TL;DR

Probabilistic-FF is a new algorithm that extends heuristic forward search to probabilistic planning with no observability, using weighted model counting to improve scalability significantly.

Contribution

It introduces Probabilistic-FF, combining heuristic search with weighted model counting for probabilistic planning without observability, achieving major scalability improvements.

Findings

Significant scalability improvements over previous methods

Effective handling of probabilistic uncertainty in planning

Identification of open issues for future research

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.