Evaluation of a Hybrid Knowledge-Based System Using Fuzzy Approach

Authors: Kamalendu Pal

Abstract:

This paper describes the main features of a knowledge-based system evaluation method. System evaluation is placed in the context of a hybrid legal decision-support system, Advisory Support for Home Settlement in Divorce (ASHSD). Legal knowledge for ASHSD is represented in two forms, as rules and previously decided cases. Besides distinguishing the two different forms of knowledge representation, the paper outlines the actual use of these forms in a computational framework that is designed to generate a plausible solution for a given case, by using rule-based reasoning (RBR) and case-based reasoning (CBR) in an integrated environment. The nature of suitability assessment of a solution has been considered as a multiple criteria decision-making process in ASHAD evaluation. The evaluation was performed by a combination of discussions and questionnaires with different user groups. The answers to questionnaires used in this evaluations method have been measured as a fuzzy linguistic term. The finding suggests that fuzzy linguistic evaluation is practical and meaningful in knowledge-based system development purpose. 

Keywords: Case-based reasoning, decision-support system, fuzzy linguistic term, rule-based reasoning, system evaluation.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1107471

References:

[1] L. Adelman, Evaluating Decision Support and Expert Systems, Wiley, New York, 1992.
[2] L. E. Allen, and C. Saxon, Some Problems in Designing Expert Systems to Aid Legal Reasoning, in ‘Proceedings of the First International Conference on Artificial Intelligence and Law’, Boston, USA, ACM Press, New York, (1987) 94-103.
[3] T. J. M. Bench-Capon, and F. Coenen, Practical Application of KBS to Law: the Crucial Role of Maintenance, in C. Noortwijk, A Schmidt, and R. Winkels (Eds.), ‘Legal Knowledge-Based Systems: Aims for Research and Development’, Lelystad, Vermande, The Netherlands, (1991) 5-17.
[4] J. F. Bard, T. A. Feo, and S. D. Holland, Reengineering and the development of a decision support system for printed wiring board assembly, IEEE Transactions on Engineering Management 42 (1995) 91-98.
[5] R. E. Bellman, and L. A. Zadeh, Local and fuzzy logics, in G. Epstein (Ed.), Modern Uses of Multiple-Valued Logic. 1977, pp.103-165.
[6] D. Borenstein, Towards a practical method to validate Decision Support Systems, Decision Support Systems 23 (1998) 227-239.
[7] J. E. Boritz, and A. K. P. Wensley, Evaluating expert systems with complex outputs – the case of audit planning, Auditing-a Journal of Practice and Theory 11 (1992) 14-29.
[8] J. Q. Chen, and S. M. Lee, An exploratory cognitive DSS for strategic decision making, Decision Support System 36 (2003) 147-160.
[9] D. Dubois, and H. Prade, Fuzzy sets in approximate reasoning, Part I: Inference with possibility distribution, Fuzzy Sets and Systems, 40, 1991, pp.143-202.
[10] M. E. Hernando, E. J. Gomez, R. Corocy, and F. del Pozo, Evaluation of DIABNET, a decision support system for therapy planning in general diabetes, Computer Methods and Programs in Biomedicine 62 (2000) 235-248.
[11] G. S. Hubona, and J. E. Blanton, Evaluating system design features, International Journal of Human-Computer Studies 44 (1996) 93-118.
[12] K. A. H. Kobbacy, N.C. Proudlove, and M. A. Harper, Towards an intelligence maintenance optimization system, Journal of the Operation Research Society 46 (1995) 831-853.
[13] S. L. Li, The development of a hybrid intelligent system for developing marketing strategy, Decision Support Systems 27 (2000) 395-409.
[14] K. Pal, An approach to legal reasoning based on a hybrid decisionsupport system, Expert Systems with Applications, 17 (1999) 1 – 12.
[15] H. J. Miser, and E. S. Quade, Handbook of System Analysis – Craft Issues and Procedural Choices, Wiley, USA, 1988.
[16] R. M. O’Keefe, and A. D. Preece, The development, validation and implementation of knowledge-based systems, European Journal of Operational Research 92 (1996) 458 – 473.
[17] R. M. O’Keefe, O. Balci, and E. P. Smith, Validating expert system performance, IEEE Intelligence Systems & Their Applications 2 (1987) 81 – 90.
[18] S. Ram, and S. Ram, Validation of expert systems for innovation management: issues, methodology, and empirical assessment, Journal of Product Innovation Management 13 (1996) 53 – 68.
[19] R. Sharda, S. H. Barr, and J. C. Mcdonnell, Decision Support System effectiveness – a review and an empirical test, Management Science 34 (1988) 139 – 159.
[20] L. Zadeh, Fuzzy sets. Information and Control, 8 (1965) 338-353.
[21] L. Zadeh, The concept of a linguistic variable and its application to approximate reasoning, Information Science, 8 (1975) 199 – 249.
[22] J. Zeleznikow, and J. R. Nolan, Using soft computing to build real world intelligent Decision Support Systems in uncertain domains, Decision Support Systems 31 (2001) 263 – 285.
[23] H. J. Zimmermann, Fuzzy set theory and its applications, Boston: Kluwer Academic Publishers, 1996.
[24] E. H. Levi, An Introduction to legal reasoning, The University of Chicago Press, 1948.
[25] S. J. Burton, An Introduction to Law and Legal Reasoning, Little, Brown and Company, 1985.
[26] P. Johnson, and D. Mead, Legislative knowledge base systems for public administration – some practical issues, Proceedings of the Third International Conference on AI and Law, 1991, pp. 108-117.
[27] R. H. Michaelson, A Knowledge-based system for individual income and transfer tax planning. Ph.D. thesis, Department of Computer Science, University of Illinois, 1982, Illinois, USA.
[28] M. J. Sergot, R. Kowalski, F. Kriwaczek, P. Hammon, and H. T. Cory, The British nationality act as a logic program, Communications of the ACM, 1986, 29, pp.370-386.
[29] K. D. Ashley, Modelling legal argument: reasoning with cases and hypotheticals, Ph.D. thesis, Department of Computer and Information Science, University of Massachusetts, 1987, Amherst, USA.
[30] W. M. Bain, Case-based reasoning: a computer model of subjective assessment. Ph.D. thesis, Department of Computer Science, Yale University, 1986, New Haven, USA.
[31] B. B. Cuthill, Using a multi-layered approach to representing Tort law cases for CBR, Proceedings of AAAI Case-based reasoning workshop, 1993, Washington, pp. 41-47.
[32] E. L. Rissland and K. D. Ashley, HYPO: a precedent-based legal reasoning, In G. Vandenberghe (Ed), Advanced topics of law and information technology, 1989, Dordrecht: Kluwer, pp. 213.
[33] L. K. Branting, Integrating rules and precedents for classification and explanation: automating legal analysis. Ph.D. thesis, Department of Computer Science, University of Texas, 1991, Austin, USA.
[34] J. Zeleznikow, and A Stranieri, The split-up system: integrating neural nets and rule-based reasoning in the legal domain, Proceedings of the Fifth International Conference on AI and Law, University of Maryland, 1995, New York: ACM Press, pp. 185.
[35] T. H. Chang, and T. C. Wang, Using the fuzzy multi-criteria decisionmaking approach for measuring the possibility of successful knowledge management, Information Sciences, 2009, 179, pp.355-370.
[36] K. Shehzad, and M. Y. Javed, Multithreaded Fuzzy Logic based Web Services Mining Framework, European Journal of Scientific Research, 2010, 4, pp. 632-644.
[37] T. Y. Hsieh, S. T. Lu, and G,H. Tzeng, Fuzzy MCDM approach for planning and design tenders selection in public office buildings, International Journal of Project Management, 22(7), 2004, pp. 573-584.