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Linked production rules: controlling inference with knowledge

Citation

Compton, P and Kim, YS and Kang, BH, Linked production rules: controlling inference with knowledge, 2014 Pacific Rim Knowledge Acquisition Workshop (PKAW 2014) Proceedings, 1-2 December 2014, Gold Coast, Australia, pp. 84-98. ISSN 0302-9743 (2014) [Refereed Conference Paper]

Copyright Statement

Copyright 2014 Springer International Publishing Switzerland

DOI: doi:10.1007/978-3-319-13332-4_8

Abstract

A key insight in artificial intelligence, which has been the foundation of expert systems and now business-rule systems, is that reasoning or inference can be separated from the domain knowledge being reasoned about. We suggest that the knowledge acquisition and maintenance problems that arise, might result from too great a separation of knowledge and inference. We propose Linked Production Rules, where each rule evaluated directs the next step of inference and the inference engine has no meta-heuristics or conflict resolution strategy. We suggest that this loses none of the power of conventional inference but may greatly improve knowledge acquisition and maintenance since various Ripple-Down Rule knowledge acquisition methods, which have had some success in facilitating knowledge maintenance can be described as specific instances of Linked Production Rules. Finally the Linked Production Rule approach suggests the possibility of a generalized Ripple-Down Rule method applicable to a wide range of problem types.

Item Details

Item Type:Refereed Conference Paper
Keywords:inference engine, declarative knowledge, conflict-resolution, problem-solving methods, Ripple-Down Rules
Research Division:Information and Computing Sciences
Research Group:Computer Software
Research Field:Computer Software not elsewhere classified
Objective Division:Information and Communication Services
Objective Group:Computer Software and Services
Objective Field:Computer Software and Services not elsewhere classified
Author:Kim, YS (Dr Yang Kim)
Author:Kang, BH (Professor Byeong Kang)
ID Code:98401
Year Published:2014
Deposited By:Computing and Information Systems
Deposited On:2015-02-13
Last Modified:2017-11-13
Downloads:0

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