Decision-Theoretic Planning in the Graphplan Framework
نویسندگان
چکیده
We have developed a decision-theoretic planner based upon the Graphplan planning algorithm, DT-Graphplan. DT-Graphplan reasons about probabilities, costs, and rewards at a propositional level, reconstructing limited state information. We are applying the planner to our robot task architecture to function on a miniature golf domain. By incorporating decision theory into planning, we seek to reduce the representational gap between behavior-based robotic controllers and constraint-based symbolic planners.
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