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Probabilistic Temporal Planning

Planning research has explored the issues that arise when planning with concurrent and durative actions. Separately, planners that can cope with probabilistic effects have also been created. However, few attempts have been made to combine both probabilistic effects and concurrent durative actions into a single planner. The principal one of which we are aware was targeted at a specific domain. We present a unified framework for probabilistic temporal planning. This framework supports actions with different duration probabilistic outcomes, and does not restrict an action’s effects to its start and end. We have tailored a deterministic search algorithm specifically for the framework. It combines elements of both LRTDP and AO*, and uses a search space designed to reduce the impact of exponential growth. Most search algorithms can benefit from the use of heuristics. We show some ways of applying heuristics to probabilistic temporal planning. This includes a framework for applying heuristics based on the planning graph data structure. The Planning Domain Definition Language (PDDL) is considered to be the standard language for defining planning domains and their associated problems. We present an extension to PDDL that supports probabilistic temporal planning.