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A robust approach to robot team learning
Institute for Aerospace Studies, University of Toronto.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Space Technology.
Number of Authors: 2
2016 (English)In: Autonomous Robots, ISSN 0929-5593, E-ISSN 1573-7527, Vol. 40, no 8, 1441-1457 p.Article in journal (Refereed) Published
Abstract [en]

The paper achieves two outcomes. First, it summarizes previous work on concurrent Markov decision processes (CMDPs) currently demonstrated for use with multi-agent foraging problems. When using CMDPs, each agent models the environment using two Markov decision process (MDP). The two MDPs characterize a multi-agent foraging problem by modeling both a single-agent foraging problem, and multi-agent task allocation problem, for each agent. Second, the paper studies the effects of state uncertainty on a heterogeneous robot team that utilizes the aforementioned CMDP modelling approach. Furthermore, the paper presents a method to maintain performance despite state uncertainty. The resulting robust concurrent individual and social learning (RCISL) mechanism leads to an enhanced team learning behaviour despite state uncertainty. The paper analyzes the performance of the concurrent individual and social learning mechanism with and without a particle filter for a heterogeneous foraging scenario. The RCISL mechanism confers statistically significant performance improvements over the CISL mechanism

Place, publisher, year, edition, pages
2016. Vol. 40, no 8, 1441-1457 p.
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Onboard space systems
Identifiers
URN: urn:nbn:se:ltu:diva-5225DOI: 10.1007/s10514-015-9524-2ISI: 000386705700005Local ID: 34493a50-c65d-480d-b57b-c62a88cfceecOAI: oai:DiVA.org:ltu-5225DiVA: diva2:978099
Note

Validerad; 2016; Nivå 2; 2016-11-02 (andbra)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2017-10-19Bibliographically approved

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