Adaptive Switching Behavioral Strategies for Effective Team Formation in Changing Environments

被引:1
|
作者
Hayano, Masashi [1 ]
Miyashita, Yuki [1 ]
Sugawara, Toshiharu [1 ]
机构
[1] Waseda Univ, Dept Comp Sci & Commun Engn, Tokyo 1698555, Japan
关键词
Allocation problem; Agent network; Bottom-up organization; Team formation; Reciprocity; STRONG RECIPROCITY; SOCIAL NORMS; COOPERATION; SELECTION;
D O I
10.1007/978-3-319-53354-4_3
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:37 / 55
页数:19
相关论文
共 50 条
  • [1] Comparative Analysis of Behavioral Models for Adaptive Learning in Changing Environments
    Markovic, Dimitrije
    Kiebel, Stefan J.
    FRONTIERS IN COMPUTATIONAL NEUROSCIENCE, 2016, 10
  • [2] Adaptive Strategies for Team Formation in Minimalist Robot Swarms
    Feola, Luigi
    Trianni, Vito
    IEEE ROBOTICS AND AUTOMATION LETTERS, 2022, 7 (02) : 4079 - 4085
  • [3] The Evolution of Alternative Adaptive Strategies for Effective Communication in Noisy Environments
    Ord, Terry J.
    Charles, Grace K.
    Hofer, Rebecca K.
    AMERICAN NATURALIST, 2011, 177 (01): : 54 - 64
  • [4] Behavioral responses to changing environments
    Wong, Bob B. M.
    Candolin, Ulrika
    BEHAVIORAL ECOLOGY, 2015, 26 (03) : 665 - 673
  • [5] Distributed Adaptive Formation Control of a Team of Aerial Robots in Cluttered Environments
    Xie, Zhipeng
    Long, Youlian
    Cheng, Hui
    INTELLIGENT ROBOTICS AND APPLICATIONS, ICIRA 2019, PT III, 2019, 11742 : 544 - 558
  • [6] Rewiring Strategies for Changing Environments
    Laurier, Wim
    Vanderhulst, Geert
    Poels, Geert
    Luyten, Kris
    AMBIENT INTELLIGENCE AND FUTURE TRENDS - INTERNATIONAL SYMPOSIUM ON AMBIENT INTELLIGENCE (ISAML 2010), 2010, 72 : 45 - +
  • [7] Evolution of learning strategies in changing environments
    Bullinaria, John A.
    COGNITIVE SYSTEMS RESEARCH, 2018, 52 : 429 - 449
  • [8] Adaptive Replanning in Hard Changing Environments
    Liu, Hong
    Wan, Weiwei
    IEEE/RSJ 2010 INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2010), 2010,
  • [9] Emerging technologies for behavioral research in changing environments
    Couzin, Iain D.
    Heins, Conor
    TRENDS IN ECOLOGY & EVOLUTION, 2023, 38 (04) : 346 - 354
  • [10] THE CHANGING ROLE OF TEAM LEADERSHIP IN MULTINATIONAL PROJECT ENVIRONMENTS
    Thamhain, Hans J.
    REVISTA DE GESTAO E PROJETOS, 2012, 3 (02): : 4 - 38