Autonomous strategy determination with learning of environments in multi-agent continuous cleaning

Ayumi Sugiyama, Toshiharu Sugawara

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

    2 Citations (Scopus)

    Abstract

    With the development of robot technology, we can expect selfpropelled robots working in large areas where coordinated and collaborative behaviors by multiple robots are necessary. Thus, the learning appropriate strategy for coordination and cooperation in multiple autonomous agents is an important issue. However, conventional methods assumed that agents was given knowledge about the environment. This paper proposes a method of autonomous strategy learning for multiple agents coordination integrated with learning where are easy to become dirty in the environments using examples of continuous cleaning tasks. We found that agents with the proposed method could operate as effectively as those with the conventional method and we found that the proposed method often outperformed it in complex areas by splitting up in their works.

    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    PublisherSpringer Verlag
    Pages455-462
    Number of pages8
    Volume8861
    ISBN (Print)9783319131900
    Publication statusPublished - 2014
    Event17th International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2014 - Gold Coast
    Duration: 2014 Dec 12014 Dec 5

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume8861
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Other

    Other17th International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2014
    CityGold Coast
    Period14/12/114/12/5

    Fingerprint

    Cleaning
    Robots
    Learning Strategies
    Robot
    Autonomous agents
    Autonomous Agents
    Learning
    Strategy
    Necessary

    ASJC Scopus subject areas

    • Computer Science(all)
    • Theoretical Computer Science

    Cite this

    Sugiyama, A., & Sugawara, T. (2014). Autonomous strategy determination with learning of environments in multi-agent continuous cleaning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8861, pp. 455-462). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8861). Springer Verlag.

    Autonomous strategy determination with learning of environments in multi-agent continuous cleaning. / Sugiyama, Ayumi; Sugawara, Toshiharu.

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8861 Springer Verlag, 2014. p. 455-462 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8861).

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

    Sugiyama, A & Sugawara, T 2014, Autonomous strategy determination with learning of environments in multi-agent continuous cleaning. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 8861, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8861, Springer Verlag, pp. 455-462, 17th International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2014, Gold Coast, 14/12/1.
    Sugiyama A, Sugawara T. Autonomous strategy determination with learning of environments in multi-agent continuous cleaning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8861. Springer Verlag. 2014. p. 455-462. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
    Sugiyama, Ayumi ; Sugawara, Toshiharu. / Autonomous strategy determination with learning of environments in multi-agent continuous cleaning. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8861 Springer Verlag, 2014. pp. 455-462 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
    @inproceedings{24ce3e0c2ef1483086fd549de0c70595,
    title = "Autonomous strategy determination with learning of environments in multi-agent continuous cleaning",
    abstract = "With the development of robot technology, we can expect selfpropelled robots working in large areas where coordinated and collaborative behaviors by multiple robots are necessary. Thus, the learning appropriate strategy for coordination and cooperation in multiple autonomous agents is an important issue. However, conventional methods assumed that agents was given knowledge about the environment. This paper proposes a method of autonomous strategy learning for multiple agents coordination integrated with learning where are easy to become dirty in the environments using examples of continuous cleaning tasks. We found that agents with the proposed method could operate as effectively as those with the conventional method and we found that the proposed method often outperformed it in complex areas by splitting up in their works.",
    author = "Ayumi Sugiyama and Toshiharu Sugawara",
    year = "2014",
    language = "English",
    isbn = "9783319131900",
    volume = "8861",
    series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
    publisher = "Springer Verlag",
    pages = "455--462",
    booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

    }

    TY - GEN

    T1 - Autonomous strategy determination with learning of environments in multi-agent continuous cleaning

    AU - Sugiyama, Ayumi

    AU - Sugawara, Toshiharu

    PY - 2014

    Y1 - 2014

    N2 - With the development of robot technology, we can expect selfpropelled robots working in large areas where coordinated and collaborative behaviors by multiple robots are necessary. Thus, the learning appropriate strategy for coordination and cooperation in multiple autonomous agents is an important issue. However, conventional methods assumed that agents was given knowledge about the environment. This paper proposes a method of autonomous strategy learning for multiple agents coordination integrated with learning where are easy to become dirty in the environments using examples of continuous cleaning tasks. We found that agents with the proposed method could operate as effectively as those with the conventional method and we found that the proposed method often outperformed it in complex areas by splitting up in their works.

    AB - With the development of robot technology, we can expect selfpropelled robots working in large areas where coordinated and collaborative behaviors by multiple robots are necessary. Thus, the learning appropriate strategy for coordination and cooperation in multiple autonomous agents is an important issue. However, conventional methods assumed that agents was given knowledge about the environment. This paper proposes a method of autonomous strategy learning for multiple agents coordination integrated with learning where are easy to become dirty in the environments using examples of continuous cleaning tasks. We found that agents with the proposed method could operate as effectively as those with the conventional method and we found that the proposed method often outperformed it in complex areas by splitting up in their works.

    UR - http://www.scopus.com/inward/record.url?scp=84910120568&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=84910120568&partnerID=8YFLogxK

    M3 - Conference contribution

    SN - 9783319131900

    VL - 8861

    T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

    SP - 455

    EP - 462

    BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

    PB - Springer Verlag

    ER -