- Title
- An approach for generalising symbolic knowledge
- Creator
- Dazeley, Richard; Kang, Byeongho
- Date
- 2008
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/57133
- Identifier
- vital:3716
- Identifier
-
https://doi.org/10.1007/978-3-540-89378-3_38
- Identifier
- ISBN:9783540893776
- 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.
- Publisher
- Auckland, New Zealand : Springer
- Relation
- Paper presented at 21st Australasian Joint Conference on Artificial Intelligence, Auckland, New Zealand : 1st-5th December 2008 p. 379-385
- Rights
- Copyright Springer
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0801 Artificial Intelligence and Image Processing; Hidden context; Knowledge based systems; Knowledge representation; Ripple-down rules; Situation cognition
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