Probabilistic Planning with Information Gathering and Contingent Execution

MostAI representations and algorithms for plan generation havenot included the concept of informationproducingactions (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 CNLPalgorithm (Peot and Smith1992)for conditionedexecution. Theresult, C-BURIDAN,is an implemented planner that builds plans with probabilistic information-producingactions and contingent execution.

• Type: Conference
• Year: 1994
• Where: AIPS
• Authors: Denise Draper, Steve Hanks, Daniel S. Weld
• Keywords: AIPS 1994, Artificial Intelligence, Plan Generation Havenot, Probabilistic Planning Algorithm, Wepresent Planning Representation