APPSSAT: Approximate Probabilistic Planning Using Stochastic Satisfiability

Abstract

We describe APPSSAT, an approximate probabilistic contingent planner based on ZANDER, a probabilistic contingent planner that operates by converting the planning problem to a stochastic satisfiability (Ssat) problem and solving that problem instead [1]. The values of some of the variables in an Ssat instance are probabilistically determined; APPSSAT considers the most likely instantiations of these variables (the most probable situations facing the agent) and attempts to construct an approximation of the optimal plan that succeeds under those circumstances, improving that plan as time permits. Given more time, less likely instantiations/situations are considered and the plan is revised as necessary. In some cases, a plan constructed to address a relatively low percentage of possible situations will succeed for situations not explicitly considered as well, and may return an optimal or near-optimal plan. This means that APPSSAT can sometimes find optimal plans faster than ZANDER. And the anytime quality of APPSSAT means that suboptimal plans could be efficiently derived in larger time-critical domains in which ZANDER might not have sufficient time to calculate the optimal plan. We describe some preliminary experimental results and suggest further work needed to bring APPSSAT closer to attacking real-world problems.