A Multi-Agent Architecture Integrating Learning and Fuzzy Techniques for Landmark-Based Robot Navigation

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/10261/2984

Title:	A Multi-Agent Architecture Integrating Learning and Fuzzy Techniques for Landmark-Based Robot Navigation
Authors:	Fulbright Commission Plan Nacional de Investigación Científica y Desarrollo (España) Generalidad de Cataluña
Keywords:	Artificial Intelligence Machine Learning Robotics Multiagent Systems Fuzzy Logic
Publisher:	Springer
Description:	La publicación original está disponible en www.springerlink.com. This paper extends a navigation system implemented as a multi-agent system (MAS). The arbitration mechanism controlling the interactions between the agents was based on manually-tuned bidding functions. A difficulty with hand-tuning is that it is hard to handle situations involving complex tradeoffs. In this paper we explore the suitability of reinforcement learning for automatically tuning agents within a MAS to optimize a complex tradeoff, namely the camera use. Fullbright Joint Research Project and Plan Nacional Project DPI 2000-1352-C02-02. Dídac Busquets holds the CIRIT doctoral scholarship 2000FI-00191. Peer reviewed
URI:	http://dspace.mediu.edu.my:8181/xmlui/handle/10261/2984
Other Identifiers:	Topics in Artificial Intelligence, 5th Catalonian Conference on AI, CCIA 2002 Castellón, Spain, October 2002. Proceedings. Lecture Notes in Artificial Intelligence Vol. 2504, p.p.: 269-281, Springer-Verlag, 2002. 0302-9743 http://hdl.handle.net/10261/2984
Appears in Collections:	Digital Csic

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