Translation of graph-based knowledge representation in multi-agent system

被引:4
|
作者
Kotulski, Leszek [1 ]
Sedziwy, Adam [1 ]
Strug, Barbara [1 ]
机构
[1] AGH Univ Sci & Technol, Dept Appl Comp Sci, PL-30059 Krakow, Poland
关键词
D O I
10.1016/j.procs.2014.05.094
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Agents provide a feasible mean for maintaining and manipulating large scale data. This paper deals with the problem of information exchange between different agents. It uses graph based formalism for the representation of knowledge maintained by an agent and graph transformations as a mean of knowledge exchange. Such a rigorous formalism ensures the cohesion of graph-based knowledge held by agents after each modification and exchange action. The approach presented in this paper is illustrated by a case study dealing with the problem of personal data held in different places (maintained by different agents) and the process of transmitting such information (1).
引用
收藏
页码:1048 / 1056
页数:9
相关论文
共 50 条
  • [31] VillagerAgent: A Graph-Based Multi-Agent Framework for Coordinating Complex Task Dependencies in Minecraft
    Dong, Yubo
    Zhu, Xukun
    Pan, Zhengzhe
    Zhu, Linchao
    Yang, Yi
    FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: ACL 2024, 2024, : 16290 - 16314
  • [32] Graph-based Selection-Activation Reinforcement Learning for Heterogenous Multi-agent Collaboration
    Chen, Hao-Xiang
    Zhang, Xi-Wen
    Shen, Jun-Nan
    Chinese Control Conference, CCC, 2024, : 5835 - 5840
  • [33] Distributed Knowledge Engineering and Evidence-Based Knowledge Representation in Multi-agent Systems
    Kolonin, Anton
    KNOWLEDGE ENGINEERING AND SEMANTIC WEB, KESW 2015, 2015, 518 : 291 - 300
  • [34] Knowledge representation for multi-agent negotiations in virtual enterprises
    Wang, X. H.
    Wong, T. N.
    Wang, G.
    INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2011, 49 (14) : 4275 - 4297
  • [35] Towards Self-X cognitive manufacturing network: An industrial knowledge graph-based multi-agent reinforcement learning approach
    Zheng, Pai
    Xia, Liqiao
    Li, Chengxi
    Li, Xinyu
    Liu, Bufan
    JOURNAL OF MANUFACTURING SYSTEMS, 2021, 61 : 16 - 26
  • [36] EFFECTIVE GRAPH REPRESENTATION SUPPORTING MULTI-AGENT DISTRIBUTED COMPUTING
    Sedziwy, Adam
    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL, 2014, 10 (01): : 101 - 113
  • [37] Towards the Knowledge-Based Multi-Agent System Identification
    Chernyshov, K. R.
    PROCEEDINGS OF THE 2015 10TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS, 2015, : 399 - 404
  • [38] The design of a new knowledge discovery system based on multi-agent
    You, Fucheng
    Song, Wei
    Yang, Bingru
    Yang, GuoWei
    Proceedings of 2006 International Conference on Artificial Intelligence: 50 YEARS' ACHIEVEMENTS, FUTURE DIRECTIONS AND SOCIAL IMPACTS, 2006, : 26 - 29
  • [39] Multi-agent Based Clinical Knowledge Representation with Its Dynamic Parse and Execution
    Hu, Yumin
    Xiao, Liang
    Liu, Xing
    Liu, Jianzhou
    Yan, Zhenzhen
    Wei, Qiuju
    Chen, Haifeng
    HEALTH INFORMATION SCIENCE, HIS 2014, 2014, 8423 : 248 - 260
  • [40] GMIX: Graph-based spatial-temporal multi-agent reinforcement learning for dynamic electric vehicle dispatching system
    Zhou, Tao
    Kris, M. Y. Law
    Creighton, Douglas
    Wu, Changzhi
    TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2022, 144