A framework for building knowledge-bases under uncertainty

Authors: Santos E.; Santos E. S.

Source: Journal of Experimental & Theoretical Artificial Intelligence, Volume 11, Number 2, 1 April 1999 , pp. 265-286(22)

Publisher: Taylor and Francis Ltd

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Abstract:

Managing uncertainty during the knowledge engineering process from elicitation to validation and verification requires a flexible, intuitive, and semantically sound knowledge representation. This is especially important since this process is typically highly interactive with the human user to add, update, and maintain knowledge. In this paper, we present a model of knowledge representation called Bayesian Knowledge-Bases (BKBs). It unifies a 'if-then' style rules with probability theory. We also consider the computational efficiency of reasoning over BKBs. We can show that through careful construction of the knowledge-base, reasoning is computationally tractable and can in fact be polynomial-time. BKBs are currently fielded in the PESKI intelligent system development environment.

Keywords: KNOWLEDGE; REPRESENTATION; REASONING; UNDER; UNCERTAINTY; PROBABILISTIC; REASONING; COMPLEXITY

Language: English

Document Type: Research article

Publication date: 1999-04-01

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