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. Introduction This paper discusses DT-Graphplan, a decision-theoretic planner that we use as the planning and sequencing layers for a layered robotic architecture. By using a planner at both the symbolic planner level and the sequencer level we hope for a reduction of the work needed to reconfigure a robot for a new task. We will verify this by creating one set of behavior controllers for our robots and demonstrating the effectiveness of the controllers on multiple diverse plans..
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.
