Adaptive switching behavioral strategies for effective team formation in changing environments

Masashi Hayano, Yuki Miyashita, Toshiharu Sugawara

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

    1 Citation (Scopus)

    Abstract

    This paper proposes a control method for in agents by switching their behavioral strategy between rationality and reciprocity depending on their internal states to achieve efficient team formation. Advances in computer science, telecommunications, and electronic devices have led to proposals of a variety of services on the Internet that are achieved by teams of different agents. To provide these services efficiently, the tasks to achieve them must be allocated to appropriate agents that have the required capabilities, and the agents must not be overloaded. Furthermore, agents have to adapt to dynamic environments, especially to frequent changes in workload. Conventional decentralized allocation methods often lead to conflicts in large and busy environments because high-capability agents are likely to be identified as the best team member by many agents, resulting in the entire system becoming inefficient due to the concentration of task allocation when the workload becomes high. Our proposed agents switch their strategies in accordance with their local evaluation to avoid conflicts occurring in busy environments. They also establish an organization in which a number of groups are autonomously generated in a bottom-up manner on the basis of dependability to avoid conflicts in advance while ignoring tasks allocated by undependable/unreliable agents. We experimentally evaluated our method in static and dynamic environments where the number of tasks varied.

    Original languageEnglish
    Title of host publicationAgents and Artificial Intelligence - 8th International Conference, ICAART 2016, Revised Selected Papers
    PublisherSpringer Verlag
    Pages37-55
    Number of pages19
    Volume10162 LNAI
    ISBN (Print)9783319533537
    DOIs
    Publication statusPublished - 2017
    Event8th International Conference on Agents and Artificial Intelligence, ICAART 2016 - Rome, Italy
    Duration: 2016 Feb 242016 Feb 26

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume10162 LNAI
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Other

    Other8th International Conference on Agents and Artificial Intelligence, ICAART 2016
    CountryItaly
    CityRome
    Period16/2/2416/2/26

    Fingerprint

    Dynamic Environment
    Workload
    Strategy
    Task Allocation
    Dependability
    Reciprocity
    Computer science
    Bottom-up
    Rationality
    Telecommunication
    Telecommunications
    Decentralized
    Switch
    Switches
    Computer Science
    Internet
    Likely
    Entire
    Electronics
    Internal

    Keywords

    • Agent network
    • Allocation problem
    • Bottom-up organization
    • Reciprocity
    • Team formation

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)

    Cite this

    Hayano, M., Miyashita, Y., & Sugawara, T. (2017). Adaptive switching behavioral strategies for effective team formation in changing environments. In Agents and Artificial Intelligence - 8th International Conference, ICAART 2016, Revised Selected Papers (Vol. 10162 LNAI, pp. 37-55). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10162 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-53354-4_3

    Adaptive switching behavioral strategies for effective team formation in changing environments. / Hayano, Masashi; Miyashita, Yuki; Sugawara, Toshiharu.

    Agents and Artificial Intelligence - 8th International Conference, ICAART 2016, Revised Selected Papers. Vol. 10162 LNAI Springer Verlag, 2017. p. 37-55 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10162 LNAI).

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

    Hayano, M, Miyashita, Y & Sugawara, T 2017, Adaptive switching behavioral strategies for effective team formation in changing environments. in Agents and Artificial Intelligence - 8th International Conference, ICAART 2016, Revised Selected Papers. vol. 10162 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10162 LNAI, Springer Verlag, pp. 37-55, 8th International Conference on Agents and Artificial Intelligence, ICAART 2016, Rome, Italy, 16/2/24. https://doi.org/10.1007/978-3-319-53354-4_3
    Hayano M, Miyashita Y, Sugawara T. Adaptive switching behavioral strategies for effective team formation in changing environments. In Agents and Artificial Intelligence - 8th International Conference, ICAART 2016, Revised Selected Papers. Vol. 10162 LNAI. Springer Verlag. 2017. p. 37-55. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-53354-4_3
    Hayano, Masashi ; Miyashita, Yuki ; Sugawara, Toshiharu. / Adaptive switching behavioral strategies for effective team formation in changing environments. Agents and Artificial Intelligence - 8th International Conference, ICAART 2016, Revised Selected Papers. Vol. 10162 LNAI Springer Verlag, 2017. pp. 37-55 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
    @inproceedings{d5ad022a5fdd47af9bbc761e0c5264d6,
    title = "Adaptive switching behavioral strategies for effective team formation in changing environments",
    abstract = "This paper proposes a control method for in agents by switching their behavioral strategy between rationality and reciprocity depending on their internal states to achieve efficient team formation. Advances in computer science, telecommunications, and electronic devices have led to proposals of a variety of services on the Internet that are achieved by teams of different agents. To provide these services efficiently, the tasks to achieve them must be allocated to appropriate agents that have the required capabilities, and the agents must not be overloaded. Furthermore, agents have to adapt to dynamic environments, especially to frequent changes in workload. Conventional decentralized allocation methods often lead to conflicts in large and busy environments because high-capability agents are likely to be identified as the best team member by many agents, resulting in the entire system becoming inefficient due to the concentration of task allocation when the workload becomes high. Our proposed agents switch their strategies in accordance with their local evaluation to avoid conflicts occurring in busy environments. They also establish an organization in which a number of groups are autonomously generated in a bottom-up manner on the basis of dependability to avoid conflicts in advance while ignoring tasks allocated by undependable/unreliable agents. We experimentally evaluated our method in static and dynamic environments where the number of tasks varied.",
    keywords = "Agent network, Allocation problem, Bottom-up organization, Reciprocity, Team formation",
    author = "Masashi Hayano and Yuki Miyashita and Toshiharu Sugawara",
    year = "2017",
    doi = "10.1007/978-3-319-53354-4_3",
    language = "English",
    isbn = "9783319533537",
    volume = "10162 LNAI",
    series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
    publisher = "Springer Verlag",
    pages = "37--55",
    booktitle = "Agents and Artificial Intelligence - 8th International Conference, ICAART 2016, Revised Selected Papers",
    address = "Germany",

    }

    TY - GEN

    T1 - Adaptive switching behavioral strategies for effective team formation in changing environments

    AU - Hayano, Masashi

    AU - Miyashita, Yuki

    AU - Sugawara, Toshiharu

    PY - 2017

    Y1 - 2017

    N2 - This paper proposes a control method for in agents by switching their behavioral strategy between rationality and reciprocity depending on their internal states to achieve efficient team formation. Advances in computer science, telecommunications, and electronic devices have led to proposals of a variety of services on the Internet that are achieved by teams of different agents. To provide these services efficiently, the tasks to achieve them must be allocated to appropriate agents that have the required capabilities, and the agents must not be overloaded. Furthermore, agents have to adapt to dynamic environments, especially to frequent changes in workload. Conventional decentralized allocation methods often lead to conflicts in large and busy environments because high-capability agents are likely to be identified as the best team member by many agents, resulting in the entire system becoming inefficient due to the concentration of task allocation when the workload becomes high. Our proposed agents switch their strategies in accordance with their local evaluation to avoid conflicts occurring in busy environments. They also establish an organization in which a number of groups are autonomously generated in a bottom-up manner on the basis of dependability to avoid conflicts in advance while ignoring tasks allocated by undependable/unreliable agents. We experimentally evaluated our method in static and dynamic environments where the number of tasks varied.

    AB - This paper proposes a control method for in agents by switching their behavioral strategy between rationality and reciprocity depending on their internal states to achieve efficient team formation. Advances in computer science, telecommunications, and electronic devices have led to proposals of a variety of services on the Internet that are achieved by teams of different agents. To provide these services efficiently, the tasks to achieve them must be allocated to appropriate agents that have the required capabilities, and the agents must not be overloaded. Furthermore, agents have to adapt to dynamic environments, especially to frequent changes in workload. Conventional decentralized allocation methods often lead to conflicts in large and busy environments because high-capability agents are likely to be identified as the best team member by many agents, resulting in the entire system becoming inefficient due to the concentration of task allocation when the workload becomes high. Our proposed agents switch their strategies in accordance with their local evaluation to avoid conflicts occurring in busy environments. They also establish an organization in which a number of groups are autonomously generated in a bottom-up manner on the basis of dependability to avoid conflicts in advance while ignoring tasks allocated by undependable/unreliable agents. We experimentally evaluated our method in static and dynamic environments where the number of tasks varied.

    KW - Agent network

    KW - Allocation problem

    KW - Bottom-up organization

    KW - Reciprocity

    KW - Team formation

    UR - http://www.scopus.com/inward/record.url?scp=85012951152&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=85012951152&partnerID=8YFLogxK

    U2 - 10.1007/978-3-319-53354-4_3

    DO - 10.1007/978-3-319-53354-4_3

    M3 - Conference contribution

    AN - SCOPUS:85012951152

    SN - 9783319533537

    VL - 10162 LNAI

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

    SP - 37

    EP - 55

    BT - Agents and Artificial Intelligence - 8th International Conference, ICAART 2016, Revised Selected Papers

    PB - Springer Verlag

    ER -