Evolving connectionist systems based role allocation of robots for soccer playing

dc.contributor.authorHuang, L.
dc.contributor.authorSong, Q.
dc.contributor.authorKasabov, N
dc.date.accessioned2009-05-27T22:18:50Z
dc.date.available2009-05-27T22:18:50Z
dc.date.copyright2005
dc.date.created2005
dc.date.issued2005
dc.description.abstractFor a group of robots (multi-agents) to complete a task, it is important for each of them to play a certain role changing with the environment of the task. One typical example is robotic soccer in which a team of mobile robots perform soccer playing behaviors. Traditionally, a robot's role is determined by a closed-form function of a robot's postures relative to the target which usually cannot accurately describe real situations. In this paper, the robot role allocation problem is converted to the one of pattern classification. Evolving classification function (ECF), a special evolving connectionist systems (ECOS), is used to identify the suitable role of a robot from the data collected from the robot system in real time. The software and hardware platforms are established for data collection, learning and verification for this approach. The effectiveness of the approach are verified by the experimental studies. ©2005 IEEE.
dc.identifier.doi10.1109/.2005.1466988
dc.identifier.urihttps://hdl.handle.net/10292/597
dc.publisherIEEE
dc.rights©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
dc.rights.accessrightsOpenAccess
dc.source20th IEEE International Symposium on Intelligent Control, ISIC '05 and the 13th Mediterranean Conference on Control and Automation, MED '05, 2005, 36-40
dc.titleEvolving connectionist systems based role allocation of robots for soccer playing
dc.typeConference Proceedings
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