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J. Ma, G. Zhang and J. Lu, “A State-Based Knowledge Representation Approach for Information Logical Inconsistency Detection in Warning Systems,” Knowledge-Based Systems, Vol. 23, No. 2, 2010, pp. 254-272. doi:10.1016/j.knosys.2009.05.010
has been cited by the following article:
TITLE: Improving Rule Base Quality to Enhance Production Systems Performance
AUTHORS: Nabil Arman
KEYWORDS: Production System; Rule Base Quality; Inference Short-Cut Rules; Inference Chains
JOURNAL NAME: International Journal of Intelligence Science, Vol.3 No.1, December 7, 2012
ABSTRACT: Production systems have a special value since they are used in state-space searching algorithms and expert systems in addition to their use as a model for problem solving in artificial intelligence. Therefore, it is of high importance to consider different techniques to improve their performance. In this research, rule base is the component of the production system that we aim to focus on. This work therefore seeks to investigate this component and its relationship with other components and demonstrate how the improvement of its quality has a great impact on the performance of the production system as a whole. In this paper, the improvement of rule base quality is accomplished in two steps. The first step involves re-writing the rules having conjunctions of literals and producing a new set of equivalent rules in which long inference chains can be obtained easily. The second step involves augmenting the rule base with inference short-cut rules devised from the long inference chains. These inference short-cut rules have a great impact on the performance of the production system. Finally, simulations are performed on randomly generated rule bases with different sizes and goals to be proved. The simulations demonstrate that the suggested enhancements are very beneficial in improving the performance of production systems.
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