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Concurrent Individual And Social Learning In Robot Teams
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Space Technology.
Institute for Aerospace Studies, University of Toronto.
Number of Authors: 2
2016 (English)In: Computational intelligence, ISSN 0824-7935, E-ISSN 1467-8640, Vol. 32, no 3, 420-438 p.Article in journal (Refereed) Published
Abstract [en]

This article discusses effective mechanisms that enable a group of robots to autonomously generate, adapt, and enhance team behaviors while improving their individual performance simultaneously. Two promising team learning concepts, namely, cooperative learning and advice-sharing, are integrated to provide a platform that encompasses a comprehensive approach to team-performance enhancement. These methods were examined in relation to the performance characteristics of standard single-robot Q-learning to ascertain whether they retain viable learning characteristics despite the integration of individual learning into team behaviors

Place, publisher, year, edition, pages
2016. Vol. 32, no 3, 420-438 p.
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Onboard space systems
Identifiers
URN: urn:nbn:se:ltu:diva-16155DOI: 10.1111/coin.12060ISI: 000383364600004Local ID: fbfc5a4f-252b-470f-b602-1ce8c20074ffOAI: oai:DiVA.org:ltu-16155DiVA: diva2:989131
Note

Validerad; 2016; Nivå 2; 20150319 (andbra)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2017-11-21Bibliographically approved

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