An Approach for Generalising Symbolic Knowledge

Authors:
Richard Dazeley;Byeong-Ho Kang
Affiliations:
School of Information Technology and Mathematical Sciences, University of Ballarat, Ballarat, Australia 7353;School of Computing and Information Systems, University of Tasmania, Hobart, 7001
Venue:
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Year:
2008

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Abstract

Many researchers and developers of knowledge based systems (KBS) have been incorporating the notion of context. However, they generally treat context as a static entity, neglecting many connectionists' work in learning hidden and dynamic contexts, which aids generalization. This paper presents a method that models hidden context within a symbolic domain achieving a level of generalisation. Results indicate that the method can learn the information that experts have difficulty providing by generalising the captured knowledge.