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Linked production rules: controlling inference with knowledge


Compton, P and Kim, YS and Kang, BH, Linked production rules: controlling inference with knowledge, Lecture Notes in Artificial Intelligence 8863: Proceedings of the 13th Pacific Rim Knowledge Acquisition Workshop (PKAW2014), 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


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:Software engineering
Research Field:Software engineering not elsewhere classified
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Information systems, technologies and services not elsewhere classified
UTAS Author:Kim, YS (Dr Yang Kim)
UTAS Author:Kang, BH (Professor Byeong Kang)
ID Code:98401
Year Published:2014
Deposited By:Information and Communication Technology
Deposited On:2015-02-13
Last Modified:2018-03-18

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