Probabilistic Planning with Information Gathering and Contingent Execution

Most AI representations and algorithms for plan generation have not included the concept of informationproducing actions (also called diagnostics, or tests, in the decision making literature). Wepresent planning representation and algorithm that models information-producing actions and constructs plans that exploit the information produced by those actions. Weextend the BURIDAN (Knshmerick et al. 1994) probabilistic planning algorithm, adapting the action representation to modelthe behavior of imperfect sensors, and combineit with a frameworkfor contingent action that extends the CNLP algorithm (Peot and Smith1992) for conditioned execution. The result, C-BURIDAN, is an implemented planner that builds plans with probabilistic information-producingactions and contingent execution. algorithm (Peot and Smith 1992). C-BURIDAN takes as input a probability distribution over initial world states, a goal expression, a set of action descriptions, and a probability threshold, and produces a contingent plan that makes the goal expression true with a Iprobability no less than the threshold.

Authors

• Denise Draper
• Steve Hanks
• Daniel S. Weld

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