Improving Rule Base Quality to Enhance Production Systems Performance


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.

Share and Cite:

N. Arman, "Improving Rule Base Quality to Enhance Production Systems Performance," International Journal of Intelligence Science, Vol. 3 No. 1, 2013, pp. 1-4. doi: 10.4236/ijis.2013.31001.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] G. Luger, “Artificial Intelligence: Structures and Strategies for Complex Problem Solving,” 6th Edition, Addison-Wesley, Boston, 2009, pp. 200-201.
[2] W. Mustafa, “Improving Performance of Production Systems by Restructuring Facts,” Abhath Al-Yarmouk Journal of Natural Sciences and Engineering, Vol. 11, No. 1A, 2002, pp. 105-120.
[3] W. Mustafa, “Optimization of Production Systems Using Genetic Algorithms,” International Journal of Computational Intelligence and Applications, Vol. 3, No. 3, 2003, pp. 233-248. doi:10.1142/S1469026803000987
[4] N. Arman, “Generating Minimum-Cost Fault-Free Rule Bases Using Minimum Spanning Trees,” International Journal of Computing and Information Sciences, Vol. 4, No. 3, 2006, pp. 114-118.
[5] N. Arman, “Fault Detection in Dynamic Rule Bases Using Spanning Trees and Disjoint Sets,” The International Arab Journal of Information Technology, Vol. 4, No. 1, 2007, pp. 67-72.
[6] N. Arman, D. Richards and D. Rine, “Structural and Syntactic Fault Correction Algorithms in Rule-Based Systems,” International Journal of Computing and Information Sciences, Vol. 2, No. 1, 2004, pp. 1-12.
[7] S. Lukichev, “Improving the Quality of Rule-Based Applications Using the Declarative Verification Approach,” International Journal of Knowledge Engineering and Data Mining, Vol. 1, No. 3, 2011, pp. 254-272. doi:10.1504/IJKEDM.2011.037646
[8] 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
[9] N. Arman, D. Rine and D. Richards, “General Fault Detection Algorithms in Constrained Rule-Based Information Distribution Systems,” Yarmouk University Research Journal, Pure Science and Engineering Series, Vol. 11, No. 2, 2003, pp. 190-203.

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.