Improvement of robustness to environmental changes by autonomous divisional cooperation in multi-agent cooperative patrol problem

Ayumi Sugiyama, Toshiharu Sugawara

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    5 Citations (Scopus)

    Abstract

    We propose a learning and negotiation method to enhance divisional cooperation and demonstrate its robustness to environmental changes in the context of the multi-agent cooperative problem. With the ongoing advances in information and communication technology, we now have access to a vast array of information, and everything has become more closely connected due to innovations such as the Internet of Things. However, this makes the tasks/problems in these environments complicated. In particular, we often require fast decision making and flexible responses to adapt to changes of environment. For these requirements, multi-agent systems have been attracting interest, but the manner in which multiple agents cooperate with each other is a challenging issue because of the computational cost, environmental complexity, and sophisticated interaction required between agents. In this work, we address a problem called the continuous cooperative patrol problem,which requires high autonomy, and propose an autonomous learning method with simple negotiation to enhance divisional cooperation for efficient work. We also investigate how this system can have high robustness, as this is one of the key elements in an autonomous distributed system. We experimentally show that agents with our method generate role sharing in a bottom-up manner for effective divisional cooperation. The results also show that two roles, specialist and generalist, emerged in a bottom-up manner, and these roles enhanced the overall efficiency and the robustness to environmental change.

    Original languageEnglish
    Title of host publicationAdvances in Practical Applications of Cyber-Physical Multi-Agent Systems
    Subtitle of host publicationThe PAAMS Collection - 15th International Conference, PAAMS 2017, Proceedings
    PublisherSpringer Verlag
    Pages259-271
    Number of pages13
    Volume10349 LNCS
    ISBN (Print)9783319599298
    DOIs
    Publication statusPublished - 2017
    Event15th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2017 - Porto, Portugal
    Duration: 2017 Jun 212017 Jun 23

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume10349 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

    Other15th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2017
    CountryPortugal
    CityPorto
    Period17/6/2117/6/23

    Keywords

    • Continuous patrolling
    • Divisional cooperation
    • Multi-agent system

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)

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  • Cite this

    Sugiyama, A., & Sugawara, T. (2017). Improvement of robustness to environmental changes by autonomous divisional cooperation in multi-agent cooperative patrol problem. In Advances in Practical Applications of Cyber-Physical Multi-Agent Systems: The PAAMS Collection - 15th International Conference, PAAMS 2017, Proceedings (Vol. 10349 LNCS, pp. 259-271). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10349 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-59930-4_21