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Conditional progressive planning under uncertainty

In this article, we describe a possibilistic/ probabilistic conditional planner called PTLplan. Being inspired by Bacchus and Kabanza's TLplan, PTLplan is a progressive planner that uses strategic knowledge encoded in a temporal logic to reduce its search space. Actions effects and sensing can be context dependent and uncertain, and the information the planning agent has at each point in time is represented as a set of situations with associated possibilities or probabilities. Besides presenting the planner itself -- its representation of actions and plans, and its algorithm -- we also provide some promising data from performance tests.