Abstract
Rules are widely used in artificial intelligence for modeling intelligent behavior and building expert systems. Most of the rule-based system programs are extremely computation- intensive and run quite slow. This problem may get worse when a rule-base does not fit into primary memory completely. On the surface, rules appear not fit for indexing unlike the databases where indexing looks obvious. In this paper, we present two sources of indexing— attribute level and value level indexing— which improve the run time response of the rule-based systems. A forward chaining inferencing using the indexing method proposed is also presented. Simulation results show that indexing in rulebases improves run time efficiency of large scale knowledgebased systems.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
