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Emergent Specialization in Swarm Systems

Li, Ling and Martinoli, Alcherio and Abu-Mostafa, Yaser S. (2002) Emergent Specialization in Swarm Systems. In: Intelligent Data Engineering and Automated Learning - IDEAL 2002. Lecture Notes in Computer Science. No.2412. Springer , Berlin, pp. 261-266. ISBN 978-3-540-44025-3.

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Distributed learning is the learning process of multiple autonomous agents in a varying environment, where each agent has only partial information about the global task. In this paper, we investigate the influence of different reinforcement signals (local and global) and team diversity (homogeneous and heterogeneous agents) on the learned solutions. We compare the learned solutions with those obtained by systematic search in a simple case study in which pairs of agents have to collaborate in order to solve the task without any explicit communication. The results show that policies which allow teammates to specialize find an adequate diversity of the team and, in general, achieve similar or better performances than policies which force homogeneity. However, in this specific case study, the achieved team performances appear to be independent of the locality or globality of the reinforcement signal.

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Additional Information:© 2002 Springer-Verlag Berlin Heidelberg. First Online: 20 August 2002. We would like to acknowledge Lavanya Reddy and Eric Tuttle for having implemented a first version of the Δ-method we are using in this paper. This work was supported by the Caltech Center for Neuromorphic Systems Engineering under NSF Cooperative Agreement EEC-9402726.
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Center for Neuromorphic Systems Engineering, CaltechUNSPECIFIED
Subject Keywords:Team Performance; Swarm Intelligence; Reinforcement Signal; Real Robot; Successful Collaboration
Series Name:Lecture Notes in Computer Science
Issue or Number:2412
Record Number:CaltechAUTHORS:20190702-150156265
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:96895
Deposited By: Tony Diaz
Deposited On:08 Jul 2019 17:10
Last Modified:16 Nov 2021 17:24

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