Evaluation of a Hybrid Knowledge-Based System Using Fuzzy Approach
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:70358", author = "Kamalendu Pal", title = "Evaluation of a Hybrid Knowledge-Based System Using Fuzzy Approach ", 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.", volume = "9", number = "7", pages = "1692-7", }
