Time-informed task planning in multi-agent collaboration

被引:4
作者
Maniadakis, Michail [1 ]
Hourdakis, Emmanouil [1 ]
Trahanias, Panos [1 ]
机构
[1] FORTH, Fdn Res & Technol Hellas, Iraklion, Greece
关键词
Multi-criteria planning; Time-informed planning; Daisy planner; Multi-agent collaboration; Human-robot interaction; PERCEPTION;
D O I
10.1016/j.cogsys.2016.09.004
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Human-robot collaboration requires the two sides to coordinate their actions in order to better accomplish common goals. In such setups, the timing of actions may significantly affect collaborative performance. The present work proposes a new framework for planning multi-agent interaction that is based on the representation of tasks sharing a common starting and ending point, as petals in a composite daisy graph. Coordination is accomplished through temporal constraints linking the execution of tasks. The planner distributes tasks to the involved parties sequentially. In particular, by considering the properties of the available options at the given moment, the planner accomplishes locally optimal task assignments to agents. Optimality is supported by a fuzzy theoretic representation of time intervals which enables fusing temporal information with other quantitative HRI aspects, therefore accomplishing a ranking of the available options. The current work aims at a systematic experimental assessment of the proposed framework is pursued, verifying that it can successfully cope with a wide range of HRI scenarios. (C) 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license.
引用
收藏
页码:291 / 300
页数:10
相关论文
共 50 条
  • [42] Multi-Agent Collaborative Caching Strategies in Dynamic Heterogeneous D2D Networks
    Fan, Xinglong
    Chen, Honglong
    Ni, Zhichen
    Li, Guoxin
    Sun, Haiyang
    Yu, Jiguo
    [J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2024, 73 (05) : 7204 - 7217
  • [43] A Fairness-Aware Cooperation Strategy for Multi-Agent Systems Driven by Deep Reinforcement Learning
    Liu, Zhixiang
    Shi, Huaguang
    Yan, Wenhao
    Jin, Zhanqi
    Zhou, Yi
    [J]. 2022 41ST CHINESE CONTROL CONFERENCE (CCC), 2022, : 4943 - 4948
  • [44] An intelligent integration method of AI English teaching resources information under multi-agent cooperation
    Wang, Bei
    [J]. INTERNATIONAL JOURNAL OF CONTINUING ENGINEERING EDUCATION AND LIFE-LONG LEARNING, 2024, 34 (01) : 88 - 99
  • [45] Analysis of collaborative urban public crisis governance in complex system: A multi-agent stochastic evolutionary game approach
    Shan, Shao-nan
    Zhang, Zi-cheng
    Ji, Wen-yan
    Wang, He
    [J]. SUSTAINABLE CITIES AND SOCIETY, 2023, 91
  • [46] Prevention schemes for future pandemic cases: mathematical model and experience of interurban multi-agent COVID-19 epidemic prevention
    Shi Yin
    Nan Zhang
    [J]. Nonlinear Dynamics, 2021, 104 : 2865 - 2900
  • [47] Driver profiling - Data-based identification of driver behavior dimensions and affecting driver characteristics for multi-agent traffic simulation
    Witt, Manuela
    Kompass, Klaus
    Wang, Lei
    Kates, Ronald
    Mai, Marcus
    Prokop, Guenther
    [J]. TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR, 2019, 64 : 361 - 376
  • [48] Prevention schemes for future pandemic cases: mathematical model and experience of interurban multi-agent COVID-19 epidemic prevention
    Yin, Shi
    Zhang, Nan
    [J]. NONLINEAR DYNAMICS, 2021, 104 (03) : 2865 - 2900
  • [49] Towards Open and Expandable Cognitive AI Architectures for Large-Scale Multi-Agent Human-Robot Collaborative Learning
    Papadopoulos, Georgios Th.
    Antona, Margherita
    Stephanidis, Constantine
    [J]. IEEE ACCESS, 2021, 9 : 73890 - 73909
  • [50] Predictable real-time constraints reveal anticipatory strategies of coupled planning in a sequential pick and place task
    Lewkowicz, Daniel
    Delevoye-Turrell, Yvonne N.
    [J]. QUARTERLY JOURNAL OF EXPERIMENTAL PSYCHOLOGY, 2020, 73 (04) : 594 - 616