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Organizational learning model for adaptive collective behaviors in multiple robots

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Abstract:

This paper proposes a novel organizational learning model in which multiple robots acquire their own functions for adaptive collective behaviors through local interactions among their neighbors and form an organizational structure to complete given tasks without global explicit control mechanisms or communication methods. In this paper, we focus on emergent processes in which robots dynamically form an organizational structure by acquiring their own appropriate functions to complete given tasks effectively and also focus on how organizational knowledge supports robots to reform their organizational structure. Through intensive simulations of truss construction by multiple robots, the following experimental results have suggested: (1) robots in our model acquire their own appropriate functions without global explicit control mechanisms or communication methods and form an organizational structure which completes given tasks in less steps than those with a centralized control system, and (2) organizational knowledge enables robots to complete the tasks which cannot be completed without it and contributes to reducing the steps for completing given tasks.

Document Type: Research Article

DOI: http://dx.doi.org/10.1163/156855398X00172

Affiliations: 1: ATR Human Information Processing Research Laboratories, 2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan, University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan 2: ATR Media Integration and Communications Research Laboratories, 2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan, Ritsumeikan University, 1916 Nojicho, Kusatsu, Shiga 525-8577, Japan 3: ATR Human Information Processing Research Laboratories, 2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan 4: University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan

Publication date: January 1, 1997

tandf/arb/1997/00000012/00000003/art00006
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