Incorporating decision-theoretic planning in a robot architecture |
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| Affiliation: | 1. State Key Laboratory of Software Engineering, Computer School, Wuhan University, Wuhan, 430072, China;2. Centre for Cyber Security Research, Deakin University, Australia;3. School of Information Technology, Deakin University, Burwood, VIC 3125, Australia;4. College of Computer and Information Sciences, King Saud University, Riyadh, Kingdom of Saudi Arabia;1. Department of Physics and Mathematics, South Ural State Humanitarian-Pedagogical University, Chelyabinsk 454080, Russia;2. Department of Mathematics, Chelyabinsk State University, Chelyabinsk 454080, Russia;3. School of Electrical Engineering and Computer Science, South Ural State University (national research university), Chelyabinsk 454080, Russia;4. Institute of Natural Sciences, South Ural State University (national research university), Chelyabinsk 454080, Russia;1. College of Mathematics and Statistics, Shenzhen University, Shenzhen 518060, China;2. Shenzhen Key Laboratory of Media Security, Shenzhen University, Shenzhen 518060, China;3. Institute of Intelligent Computing Science, Shenzhen University, Shenzhen 518060, China |
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| Abstract: | The goal of robotics research is to design a robot to fulfill a variety of tasks in the real world. Inherent in the real world is a high degree of uncertainty about the robot’s behavior and about the world. We introduce a robot task architecture, DTRC, that generates plans with actions that incorporate costs and uncertain effects, and states that yield rewards.The use of a decision-theoretic planner in a robot task architecture is demonstrated on the mobile robot domain of miniature golf. The miniature golf domain shows the application of decision-theoretic planning in an inherently uncertain domain, and demonstrates that by using decision-theoretic planning as the reasoning method in a robot task architecture, accommodation for uncertain information plays a direct role in the reasoning process. |
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