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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Kawakami, T. Kinoshita, M. Kakazu, Y. |
| Copyright Year | 1999 |
| Description | Author affiliation: Dept. of Ind. Eng., Hokkaido Inst. of Technol., Sapporo, Japan (Kawakami, T.) |
| Abstract | We propose a new approach to realize a reinforcement learning scheme for heterogeneous multiagent systems. In our approach, we treat the collective agents systems in which there are multiple autonomous mobile robots, and given tasks are achieved based on the collective behavior approach. Also, each agent organizes and refines its knowledge for executing its own behaviors by reinforcement learning mechanisms. Thus, we discuss the reinforcement learning mechanism by which the common knowledge is effectively learned in heterogeneous-agents systems. In our approach, a common knowledge field is generated, and then the leaned rule formed knowledge is embedded in that field. The proposed reinforcement learning mechanism is constructed based on learning classifier systems. An extended model of learning classifier systems is defined to apply the model to heterogeneous-agent systems containing the common knowledge field. We perform computer simulations for multiagent escaping problems to verify our proposed method. |
| Starting Page | 469 |
| Ending Page | 474 |
| File Size | 631535 |
| Page Count | 6 |
| File Format | |
| ISBN | 0780357310 |
| ISSN | 1062922X |
| DOI | 10.1109/ICSMC.1999.815596 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1999-10-12 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Learning Multiagent systems Controllability Computer simulation Sociology Electrical equipment industry Collaborative work Communication effectiveness Intelligent robots Uncertainty |
| Content Type | Text |
| Resource Type | Article |
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