An alternative verification and validation technique for an alternative knowledge representation and acquisition technique

Abstract Ripple-Down Rules (RDR) are an alternative from mainstream approaches to the building of knowledge based systems (KBS). RDR use simple, but reliable, techniques for Knowledge Acquisition (KA) and representation which have been shown to support the on-line development, maintenance and validation of KBS. Key features of RDR that affect the approach to Verification and Validation (V&V) are the incremental nature of KA and the maintenance, use of cases for KA and validation, the use of an exception structure for knowledge representation and the development of KBS by experts. This article describes RDR and its approach to V&V concentrating particularly on recent extensions which use Rough Set Theory for verification and Formal Concept Analysis for validation.

[1]  Peter F. Patel-Schneider,et al.  The DARPA Knowledge Sharing Effort: A Progress Report , 1997, KR.

[2]  Manfred K. Warmuth,et al.  Learning nested differences of intersection-closed concept classes , 2004, Machine Learning.

[3]  Susan Craw,et al.  Knowledge Refinement for a Design System , 1997, EKAW.

[4]  Pedro Meseguer,et al.  Incremental Verification of Rule-Based Expert Systems , 1992, ECAI.

[5]  Brian R. Gaines,et al.  Comparing the Conceptual Systems of Experts , 1989, IJCAI.

[6]  John McDermott,et al.  Preliminary steps toward a taxonomy of problem-solving methods , 1993 .

[7]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[8]  Allen Newell,et al.  The Knowledge Level , 1989, Artif. Intell..

[9]  B. Chandrasekaran,et al.  Generic Tasks in Knowledge-Based Reasoning: High-Level Building Blocks for Expert System Design , 1986, IEEE Expert.

[10]  Luc Steels The componential framework and its role in reusability , 1993 .

[11]  Osman Balci,et al.  Validating Expert System Performance , 1987, IEEE Expert.

[12]  Valerie Barr,et al.  Applications of rule-base coverage measures to expert system evaluation , 1997, Knowl. Based Syst..

[13]  Robert M. Colomb,et al.  Analysis of Redundancy in Expert Systems Case Data , 1995 .

[14]  Rudolf Wille,et al.  Lattices in Data Analysis: How to Draw Them with a Computer , 1989 .

[15]  Ronald L. Rivest,et al.  Learning decision lists , 2004, Machine Learning.

[16]  Paul Compton,et al.  Knowledge Acquisition without Analysis , 1993, EKAW.

[17]  Heikki Mannila,et al.  Learning rules with local exceptions , 1994 .

[18]  Janusz Zalewski,et al.  Rough sets: Theoretical aspects of reasoning about data , 1996 .

[19]  Rudi Studer,et al.  KARO: An Integrated Environment for Reusing Ontologies , 1994, EKAW.

[20]  Luc Steels Second Generation Expert Systems , 1987 .

[21]  X. Li Quality time-What's so bad about rule-based programming? , 1991 .

[22]  Janet L. Kolodner,et al.  Case-Based Reasoning , 1988, IJCAI 1989.

[23]  I. Rival Algorithms and Order , 1988 .

[24]  Antonis C. Kakas,et al.  Learning Non-Monotonic Logic Programs: Learning Exceptions , 1995, ECML.

[25]  Tobias Scheffer,et al.  Algebraic foundations and improved methods of induction or ripple-down rules , 1996 .

[26]  Zdzisław Pawlak,et al.  Rough sets based decision algorithm for treatment of duodenal ulcer by HSV , 1987 .

[27]  Byeong Ho Kang,et al.  The Use of Simulated Experts in Evaluating Knowledge Acquisition , 1995 .

[28]  Byunghoon Kang Validating Knowledge Acquisition: Multiple Classification Ripple Down Rules PhD Thesis , 1996 .

[29]  Edward H. Shortliffe,et al.  An Approach to Verifying Completeness and Consistency in a Rule-Based Expert System , 1982, AI Mag..

[30]  Guus Schreiber,et al.  KADS : a principled approach to knowledge-based system development , 1993 .

[31]  I. Rosenfield The invention of memory : a new view of the brain , 1988 .

[32]  Byeong Ho Kang,et al.  Verification and validation with ripple-down rules , 1996, Int. J. Hum. Comput. Stud..

[33]  Steven A. Vere,et al.  Multilevel Counterfactuals for Generalizations of Relational Concepts and Productions , 1980, Artif. Intell..

[34]  Debbie Richards,et al.  The Reuse of Ripple Down Rule Knowledge Bases : Using Machine Learning to Remove Repetition , 1996 .

[35]  Michael J. Pazzani,et al.  Revision of Production System Rule-Bases , 1994, ICML.

[36]  Avelino J. Gonzalez,et al.  The Engineering of Knowledge-Based Systems , 1993 .

[37]  R. Wille Concept lattices and conceptual knowledge systems , 1992 .

[38]  Alun D. Preece,et al.  Principles and practice in verifying rule-based systems , 1992, Knowl. Eng. Rev..

[39]  Geoffrey I. Webb,et al.  Recent progress in machine-expert collaboration for knowledge acquisition , 1995 .

[40]  Paul Compton,et al.  Knowledge based systems that have some idea of their limits , 1996 .

[41]  Samson W. Tu,et al.  A multiple-method knowledge-acquisition shell for the automatic generation of knowledge-acquisition tools , 1992 .

[42]  Harold J. Steudel,et al.  A Decision-Table-Based Processor for Checking Completeness and Consistency in Rule-Based Expert Systems , 1987, Int. J. Man Mach. Stud..

[43]  Byeong Ho Kang,et al.  Multiple Classification Ripple Down Rules : Evaluation and Possibilities , 2000 .

[44]  P. Compton,et al.  A philosophical basis for knowledge acquisition , 1990 .

[45]  K. VanLehn Architectures for Intelligence , 1999 .

[46]  R. Słowiński,et al.  Rough sets approach to analysis of data from peritoneal lavage in acute pancreatitis. , 1988, Medical informatics = Medecine et informatique.

[47]  Tin A. Nguyen,et al.  Knowledge base verification , 1987 .

[48]  E. Zalta,et al.  Intensional Logic and the Metaphysics of Intentionality. , 1991 .

[49]  Elliot Soloway,et al.  Assessing the Maintainability of XCON-in-RIME: Coping with the Problems of a VERY Large Rule-Base , 1987, AAAI.

[50]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[51]  Ramanathan V. Guha,et al.  o CYC : A MID-TERM , 2007 .

[52]  Václav Chvalovský,et al.  Decision tables , 1983, Softw. Pract. Exp..

[53]  G Edwards,et al.  Peirs: A pathologist‐maintained expert system for the interpretation of chemical pathology reports , 1993, Pathology.

[54]  J. Krysiński Rough sets approach to the analysis of the structure-activity relationship of quaternary imidazolium compounds. , 1990, Arzneimittel-Forschung.

[55]  Paul Compton,et al.  The (Extensive) Implications of Evaluation on the Development of Knowledge-Based Systems , 1995 .