A hybrid POMDP-BDI agent architecture with online stochastic planning and plan caching |
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| Institution: | 1. School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, South Africa;2. Department of Computer Science, University of Cape Town, South Africa;3. Centre for Artificial Intelligence Research, CSIR Meraka, South Africa;1. Department of Chemistry, Badji-Mokhtar University, Annaba, Algeria;2. Laboratory of Computational Chemistry and Nanostructures, BP: 401, University of Guelma, Algeria;3. Applied Organic Chemistry Laboratory, Department of Chemistry, Faculty of Science, Badji-Mokhtar University, BP 12 Annaba, Algeria;1. Department of Electrical and Computer Engineering, Ryerson University, 350 Victoria Street, Toronto, Ontario, Canada M5B2K3;2. Toby Hull Family Cardiac Fibrillation Management Laboratory, Toronto General Hospital, 200 Elizabeth Street, Toronto, Ontario, Canada M5G2C4;3. Princess Margaret Cancer Centre, Princess Margaret Hospital, 610 University Avenue, Toronto, Ontario, Canada M5G2M9;1. Department of Computer Science, University of Copenhagen, Sigurdsgade 41, 2200 Copenhagen, Denmark;2. Institute for Computing and Information Sciences, Radboud University Nijmegen, Toernooiveld 212, 6525 EC Nijmegen, Netherlands;3. Space Science Center, University of Copenhagen, 2100 Copenhagen, Denmark;1. School of Mathematical Sciences and Fujian Provincial Key Laboratory of Mathematical Modeling and High-Performance Scientific Computing, Xiamen University, Xiamen, Fujian 361005, China;2. Department of Mathematics, University of Kansas, Lawrence, KS 66045, USA;1. Department of Physics and Astronomy, University of Kentucky, Lexington, KY 40506-0055, USA;2. Department of Physics and Astronomy, Northwestern University, Evanston, IL 60208-3112, USA;3. Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY 40506-0055, USA;1. College of Information Engineering, Nanjing University of Finance and Economics, Nanjing, Jiangsu 210023, PR China;2. Department of Applied Mathematics, Nanjing University of Finance and Economics, Nanjing, Jiangsu 210023, PR China;3. School of Automation, Southeast University, Nanjing, Jiangsu 210096, PR China;4. College of Information Science and Technology, Donghua University, Shanghai 201620, PR China |
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| Abstract: | This article presents an agent architecture for controlling an autonomous agent in stochastic, noisy environments. The architecture combines the partially observable Markov decision process (POMDP) model with the belief-desire-intention (BDI) framework. The Hybrid POMDP-BDI agent architecture takes the best features from the two approaches, that is, the online generation of reward-maximizing courses of action from POMDP theory, and sophisticated multiple goal management from BDI theory. We introduce the advances made since the introduction of the basic architecture, including (i) the ability to pursue and manage multiple goals simultaneously and (ii) a plan library for storing pre-written plans and for storing recently generated plans for future reuse. A version of the architecture is implemented and is evaluated in a simulated environment. The results of the experiments show that the improved hybrid architecture outperforms the standard POMDP architecture and the previous basic hybrid architecture for both processing speed and effectiveness of the agent in reaching its goals. |
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| Keywords: | Autonomous agents POMDP BDI Satisfaction Planning Memory |
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