Multi-agent systems support for community-based learning

被引:12
|
作者
Lee, Y [1 ]
Chong, QD [1 ]
机构
[1] Univ Missouri, Sch Interdisciplinary Comp Engn, Kansas City, MO 64110 USA
关键词
community-based learning; adaptive and collaborative learning; learning middleware; multiagent; shared data model; component oriented development;
D O I
10.1016/S0953-5438(02)00057-7
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Electronic distributed learning that overlooks the physical and geographic status of learners has become a reality. Moreover, its quality has been considerably improved by utilizing recent advances in web-based technology. Various electronic learning support systems such as Internet-based tutorials and Virtual Universities have appeared in different forms and reflect advances in technology. However, there remains a huge barrier to support the shareable and collaborative learning available through virtual communities. Our solution to these problems was to develop an educational middleware, called the Community-Based Learning (CoBL) framework whose goal is to: (a) adapt to the diverse requirements of learners; (b) support shareable and collaborative learning; and (c) be capable of facilitating distributed learning over the Internet. The CoBL framework is based on: (1) agents to manage individual learners and communities of learners; (2) a shared data model for integrating heterogeneous communities; and (3) a component-oriented development approach. We have implemented the CoBL prototype system and used it for community-based leaming. (C) 2002 Elsevier Science B.V. All rights reserved.
引用
下载
收藏
页码:33 / 55
页数:23
相关论文
共 50 条
  • [31] Reactivity and safe learning in multi-agent systems
    Banerjee, Bikramjit
    Peng, Jing
    ADAPTIVE BEHAVIOR, 2006, 14 (04) : 339 - 356
  • [32] Specialization in multi-agent systems through learning
    Murciano, A
    Millan, JD
    Zamora, J
    BIOLOGICAL CYBERNETICS, 1997, 76 (05) : 375 - 382
  • [33] A HYBRID APPROACH FOR MULTI-AGENT LEARNING SYSTEMS
    Kuo, Jong Yih
    Huang, Fu Chu
    INTELLIGENT AUTOMATION AND SOFT COMPUTING, 2011, 17 (03): : 385 - 399
  • [34] A communication architecture for multi-agent learning systems
    Ireson, N
    Cao, YJ
    Bull, L
    Miles, R
    REAL-WORLD APPLICATIONS OF EVOLUTIONARY COMPUTING, PROCEEDINGS, 2000, 1803 : 255 - 266
  • [35] Multi-agent based approach to support HCI
    Zhu, Zhen
    Wang, Jing-Yan
    PROCEEDINGS OF 2006 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, 2006, : 188 - +
  • [36] Cooperative reinforcement learning in topology-based multi-agent systems
    Dan Xiao
    Ah-Hwee Tan
    Autonomous Agents and Multi-Agent Systems, 2013, 26 : 86 - 119
  • [37] Exploration Strategies for Model-based Learning in Multi-agent Systems
    Carmel D.
    Markovitch S.
    Autonomous Agents and Multi-Agent Systems, 1999, 2 (2) : 141 - 172
  • [38] Cooperative reinforcement learning in topology-based multi-agent systems
    Xiao, Dan
    Tan, Ah-Hwee
    AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS, 2013, 26 (01) : 86 - 119
  • [39] Model-based learning of interaction strategies in multi-agent systems
    Carmel, D
    Markovitch, S
    JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE, 1998, 10 (03) : 309 - 332
  • [40] HOMAN, a learning based negotiation method for holonic multi-agent systems
    Beheshti, Rahmatollah
    Mozayani, Nasser
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2014, 26 (02) : 655 - 666