Self-learning of the Containers Service Coordinator Agent in Multi-agent Automation Environment of Transit Cargo Terminal

被引:1
|
作者
Lutsan, M. V. [1 ]
Nuzhnov, E. V. [1 ]
Kureichik, V. V. [1 ]
机构
[1] Southern Fed Univ, Rostov Na Donu, Russia
关键词
Self-learning; Coordinator agent; Transit terminal; Multi-agent approach; Container; CROSS-DOCKING;
D O I
10.1007/978-3-319-18476-0_12
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The article deals with some problems of transport logistics, concerning the improvement of the organization and automation of basic processes of transit cargo terminal. The terminal operates with three-dimensional blocks which contain packaged goods: it receives arriving blocks, temporarily stores and sends them to the customers. Blocks are transported on trucks in a receptacle of limited size, conventionally called a container. When the resources of loading and unloading of containers, transport and storage are limited, and there are not enough some ordered blocks, queues of containers waiting for loading and unloading may occur. The authors applied multi-agent approach to the terminal management: the work is distributed among the four agents: containers unloading agent, warehouse agent, containers loading agent and the main coordinator agent. A new function of coordinator agent - self-learning based on the results of its previous work - is presented and described in the article. Self-learning is an important property of intelligent agent. This property can contribute to increasing the effectiveness of using the agents for the organization and automation of transit cargo terminal.
引用
收藏
页码:109 / 117
页数:9
相关论文
共 50 条
  • [1] Agent programmability in a multi-agent learning environment
    Cao, Y
    Greer, J
    ARTIFICIAL INTELLIGENCE IN EDUCATION: SHAPING THE FUTURE OF LEARNING THROUGH INTELLIGENT TECHNOLOGIES, 2003, 97 : 297 - 304
  • [2] A Self-Learning Evolutionary Multi-Agent System for Distribution Network Reconfiguration
    Sun, Hongbin
    Ding, Yongsheng
    ADVANCED MEASUREMENT AND TEST, PARTS 1 AND 2, 2010, 439-440 : 1209 - +
  • [3] Self-learning Governance of Black-Box Multi-Agent Systems
    Oesterle, Michael
    Bartelt, Christian
    Luedtke, Stefan
    Stuckenschmidt, Heiner
    COORDINATION, ORGANIZATIONS, INSTITUTIONS, NORMS, AND ETHICS FOR GOVERNANCE OF MULTI-AGENT SYSTEMS XV, 2022, 13549 : 73 - 91
  • [4] Evolutionary Game Dynamics of Multi-agent Cooperation Driven by Self-learning
    Du, Jinming
    Wu, Bin
    Wang, Long
    2013 9TH ASIAN CONTROL CONFERENCE (ASCC), 2013,
  • [5] Design of a self-learning multi-agent framework for the adaptation of modular production systems
    Daniele Scrimieri
    Shukri M. Afazov
    Svetan M. Ratchev
    The International Journal of Advanced Manufacturing Technology, 2021, 115 : 1745 - 1761
  • [6] Design of a self-learning multi-agent framework for the adaptation of modular production systems
    Scrimieri, Daniele
    Afazov, Shukri M.
    Ratchev, Svetan M.
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2021, 115 (5-6): : 1745 - 1761
  • [7] TraCo: Learning Virtual Traffic Coordinator for Cooperation with Multi-Agent Reinforcement Learning
    Liu, Weiwei
    Jing, Wei
    Gao, Lingping
    Guo, Ke
    Xu, Gang
    Liu, Yong
    CONFERENCE ON ROBOT LEARNING, VOL 229, 2023, 229
  • [8] Multi-agent Reinforcement Learning for Service Composition
    Lei, Yu
    Yu, Philip S.
    PROCEEDINGS 2016 IEEE INTERNATIONAL CONFERENCE ON SERVICES COMPUTING (SCC 2016), 2016, : 790 - 793
  • [9] A Multi-Agent Learning Model for Service Composition
    Xu, Wenbo
    Cao, Jian
    Zhao, Haiyan
    Wang, Lei
    2012 IEEE ASIA-PACIFIC SERVICES COMPUTING CONFERENCE (APSCC), 2012, : 70 - 75
  • [10] Multi-agent learning methods in an uncertain environment
    Liu, SH
    Tian, YT
    2002 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-4, PROCEEDINGS, 2002, : 650 - 654