Fuzzy cognitive modeling for argumentative agent
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Tao, Xuehong, Yelland, Nicola and Zhang, Yanchun (2012) Fuzzy cognitive modeling for argumentative agent. In: WCCI 2012 IEEE World Congress on Computational Intelligence. IEEE, Piscataway, New Jersey.
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.
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| Item type | Book Section |
| URI | https://vuir.vu.edu.au/id/eprint/22142 |
| DOI | 10.1109/FUZZ-IEEE.2012.6251204 |
| Official URL | http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arn... |
| ISBN | 9781467315074 (print), 9781467315050 (online), 9781467315067 |
| Subjects | Historical > FOR Classification > 0801 Artificial Intelligence and Image Processing Historical > FOR Classification > 1303 Specialist Studies in Education Historical > Faculty/School/Research Centre/Department > College of Education Current > Division/Research > College of Science and Engineering |
| Keywords | ResPubID25913, fuzzy cognitive maps, collaborative argumentation, modelling, intelligent tutoring systems, intelligent software agents, argumentative learning, cognitive systems, fuzzy set theory, artificial intelligence, problem solving, software agents, argumentation dialogues, deep learning, diabetes education, eHealth decision support, human cognition, learning systems, educational technologies |
| Citations in Scopus | 2 - View on Scopus |
| Download/View statistics | View download statistics for this item |
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