zander is a state-of-the-art probabilistic planner that extends the probabilistic-planning-as-stochastic-satisfiability paradigm to support contingent planning in domains where there is uncertainty in the effects of the agent’s actions and where the scope and accuracy of the agent’s observations may be insufficient to establish the agent’s current state with certainty (Majercik & Littman 1999). We describe zander and then discuss an approximation technique we are developing that will help us to scale up our SSat-based technique to large planning problems. We report results using this approximation algorithm on random SSat problems and discuss issues that arise in the application of this algorithm to SSat encodings of planning problems.
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