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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.ORCID iD: 0000-0003-4977-6339
Institute for Aerospace Studies, University of Toronto.
Number of Authors: 22016 (English)In: Computational intelligence, ISSN 0824-7935, E-ISSN 1467-8640, Vol. 32, no 3, p. 420-438Article 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, p. 420-438
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: 000383364600004Scopus ID: 2-s2.0-84924390358Local ID: fbfc5a4f-252b-470f-b602-1ce8c20074ffOAI: oai:DiVA.org:ltu-16155DiVA, id: diva2:989131
Note

Validerad; 2016; Nivå 2; 20150319 (andbra)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved

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