• DocumentCode
    2753201
  • Title

    Fuzzy cognitive modeling for argumentative agent

  • Author

    Tao, XueHong ; Yelland, Nicola ; Zhang, Yanchun

  • Author_Institution
    Centre of Appl. Inf., Victoria Univ., Melbourne, VIC, Australia
  • fYear
    2012
  • fDate
    10-15 June 2012
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    Argumentation plays an important role in promoting deep learning, fostering conceptual change and supporting problem solving. The new “learning by arguing” paradigm leads to new learning opportunities. However, due to the difficulties in modeling human cognition, there are few learning systems that can facilitate argumentation dialogues between systems and learners. Fuzzy Cognitive Map (FCM) is an effective tool in modeling human cognition. This paper proposes an FCM based argumentation model. Based on this model we design an argumentative software agent to facilitate argumentative learning. Provided with the domain knowledge and argumentation capability, the agent is able to simulate a peer learner and automatically conduct argumentative dialogues with learners. The argumentative agent can be applied in general school education as well as special domains like diabetes education and eHealth decision support.
  • Keywords
    cognitive systems; fuzzy set theory; learning (artificial intelligence); problem solving; software agents; FCM; argumentation dialogues; argumentative software agent; deep learning; diabetes education; eHealth decision support; fuzzy cognitive map; fuzzy cognitive modeling; human cognition; learning systems; problem solving; school education; Artificial intelligence; Cognition; Collaboration; Computational modeling; Diabetes; Humans; Proposals; Fuzzy cognitive map; argumentative learning; collaborative argumentation; intelligent software agent; intelligent tutoring system;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on
  • Conference_Location
    Brisbane, QLD
  • ISSN
    1098-7584
  • Print_ISBN
    978-1-4673-1507-4
  • Electronic_ISBN
    1098-7584
  • Type

    conf

  • DOI
    10.1109/FUZZ-IEEE.2012.6251204
  • Filename
    6251204