GENERATION OF TEST BASES OF RULES FOR THE ANALYSIS OF PRODUCTIVITY OF LOGICAL INFERENCE ENGINE
DOI:
https://doi.org/10.30837/ITSSI.2020.13.077Keywords:
inference engine, performance analysis (benchmarking), expert systems (rule-based system), Manners, Waltz, PaletteAbstract
The subject of research in the article are test tasks to determine the performance of logical inference engines based on rules. The purpose of the work is to create a method of forming a database of rules and a set of data for analyzing the performance of logical inference mechanisms according to the given characteristics of rule activation and the complexity of finding a way to the target conclusion. The article solves the following tasks: determining the requirements for the knowledge base to be formed; creation of a knowledge base model; creating a way to form rules; identifying ways to increase the number of rules; providing testing of logical inference mechanisms for the proposed test problem. The following methods are used: methods of comparison with the sample, graph theory, logical programming. The following results were obtained: the method provides opportunities: creation of conditions of rules that complicate the data flow network of the Rete-algorithm as much as possible; formation of test bases of rules for derivation both on logic of the first order, and on offer logic; simply increase the number of knowledge base rules while maintaining the output logic. The formation of the knowledge base is based on a graph that represents the meta-rules of mixing paints to obtain a new colour. The vertices of the graph are colour classes. Each metarule is either an edge leading to the OR vertex or a set of edges in the case of the AND vertex. Each meta-rule specifies a scheme for creating several rules, because its structural components are classes of paints. The given structure of the graph significantly complicates the logical inference, because to prove the truth of the conclusion on AND-vertices it is necessary to have the conclusions obtained in the previous steps of different search directions. Examples of rule formation are given. Target vertices are defined, which determine the simplest and most complex cases of logical inference. Conclusions: it was proposed a semantic model of the knowledge base in the form of AND/OR-graph, which allows you to test the effectiveness of the implementation of conflict resolution strategies, as well as heuristic algorithms; a method of creating tests for inference mechanisms, which allows you to generate a database of rules and a set of data of certain sizes, as well as to model the complexity of finding the target output and activating the rules. Ways to increase the number of rules of the knowledge base to complicate the problem of logical inference have been presented; formulation of tests to determine the performance of logic output mechanisms for the proposed test problem has been done.
References
Bobek, S., Misiak, P. (2017), "Framework for Benchmarking Rule-Based Inference Engines", Artificial Intelligence and Soft Computing, ICAISC 2017, Lecture Notes in Computer Science, Springer, Cham., Vol. 10246, P. 399–410. DOI: https://doi.org/10.1007/978-3-319-59060-8_36. SCOPUS.
"Clips R2 Benchmark" (2020), Production systems technologies, available at: http://www.grossweb.com/pst/clips-r2-benchmark/ (last accessed 25.09.20).
Riley, G. "Rearchitecting CLIPS" (2008), Business Rules Knowledge Base - October Rulefest 2008, available at: http://bizrules.info/conference/ORF2008DFW/GaryRiley_RearchitectingCLIPS_ORF2008.pdf (last accessed 25.09.20).
Kieman, G., de-Maindriville, C., Simon, E. (1990), "Making Deductive Database a Practical Technology: A Step Forward", Institute National de Recherche en Informatique et en Automatique, Report No. 1153, P. 237–245. DOI: https://doi.org/10.1145/93605.98733
Brant, D, Grose, T., Lofaso, B., Miranker, D. P. (1991), "Effects of Database Size on Rule System Performance: Five Case Studies", Proceedings of the 17th International Conference on Very Large Data Bases, P. 287–296.
Plant, R., Salinas, P. (1994), "Expert systems shell benchmarks: The missing comparison factor", Information & Management, https://www.sciencedirect.com/science/journal/03787206/27/2">Vol. 27, Issue 2, August 1994, P. 89–101. DOI: https://doi.org/10.1016/0378-7206(94)90009-4
Farinha, J. M. T. (2018), Asset Maintenance Engineering Methodologies, CRC Press, 322 p.
Hicks, R. C., Wright, K. (2009), "Performance Testing of Propositional Logic Inference Engines", Journal of Computer Information Systems, Published online, Vol. 49, P. 122–126.
Bergmann, G., Horváth, Á., Ráth, I., Varró, D. (2008), "A Benchmark Evaluation of Incremental Pattern Matching in Graph Transformation", Graph Transformations, ICGT 2008, Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, Vol 5214. P. 396–410. DOI: https://doi.org/10.1007/978-3-540-87405-8_27
Liang, S., Fodor, P., Wan, H., Kifer, M. (2009), "OpenRuleBench: An analysis of the performance of rule engines", Proc. 18th International Conference on World Wide Web, P. 601–610. DOI: https://doi.org/10.1145/1526709.1526790
Rattanasawad, T., Buranarach, M., Saikaew, K.R., Supnithi, T. (2018), "A Comparative Study of Rule-Based Inference Engines for the Semantic Web", IEICE Transactions on Information and Systems, January 2018, P. 82–89. DOI: https://doi.org/10.1587/transinf.2017SWP0004
Hecham, A., Croitoru M., Bisquert P. (2018), "A First Order Logic Benchmark for Defeasible Reasoning Tool Profiling", RuleML+RR, Sep 2018, Luxembourg, P. 81–97. DOI: https://doi.org/10.1007/978-3-319-99906-7_6
Hecham, A., Croitoru, M., Bisquert P. (2018), "Demonstrating a benchmark for defeasible reasoning", Computational Models of Argument, ser. Frontiers in Artificial Intelligence and Applications, Vol. 305, P. 461–462. DOI: https://doi.org/10.3233/978-1-61499-906-5-461
Hecham, A., Croitoru, M., Bisquert P. (2018), "On a flexible representation for defeasible reasoning variants", AAMAS’18: Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, P. 1123–1131.
Shapovalova, S., Mazhara, O. (2015), "Formalization of basic pattern matching algorithms in production systems", Eastern European Journal of Enterprise Technologies, No. 4/3 (76), P. 22–27. DOI: https://doi.org/10.15587/1729-4061.2015.46571
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