Using natural language to represent knowledge in an intelligent tutoring system
Pages 191 - 192
Abstract
Developing intelligent tutoring systems (ITS) for a target domain includes authoring tasks requiring long time and efforts. Letting authors use natural language to represent various kinds of knowledge is a way that any authors can add and update knowledge easily. An authoring system acquiring knowledge represented in natural language for an equation-based tutor (Pyrenees) is presented in this paper. This authoring system was designed to acquire not only problem statements, but also commonsense and domain knowledge in natural language from authors.
References
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Gordon S. Novak Jr., "Representations of Knowledge in a Program for Solving Physics Problems", IJCAI-77, Cambridge, MA, 1977, pp. 286--291.
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Alan Bundy, et. al., "Solving mechanics problems using meta-level inference", IJCAI, 1979, pp. 1017--1027.
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Steven Ritter, et. al., "Authoring Content in the PAT Algebra Tutor", Journal of Interactive Media in Education, 1998 (9), pp. 1--30.
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Kurt VanLehn., et. al., "Implicit versus explicit learning of strategies in a non-procedural cognitive skill", Intelligent Tutoring Systems: 7th Intern'l Conf. (pp. 521--530). 2004, Berlin.
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Timothy Chklovski, "Learner: a system for acquiring commonsense knowledge by analogy", K-CAP'03:4--12
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Robert Speer, "Open Mind Commons: An Inquisitive Approach to Learning Common Sense", Workshop on Common Sense and Intelligent User Interfaces, 2007.
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Bill MacCartney and Christopher D. Manning, "Modeling semantic containment and exclusion in natural language inference", Coling-08, Manchester, UK, 2008
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Index Terms
- Using natural language to represent knowledge in an intelligent tutoring system
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September 2009
222 pages
Copyright © 2009 Copyright is held by the author/owner(s).
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Association for Computing Machinery
New York, NY, United States
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Published: 01 September 2009
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K-CAP '09: Fifth International Conference on Knowledge Capture 2009
September 1 - 4, 2009
California, Redondo Beach, USA
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Overall Acceptance Rate 55 of 198 submissions, 28%
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