Enhanced Case-Based Reasoning trough Use of Argumentation and Numerical Taxonomy

Cases as used in case-based reasoning (CBR) typically record experts' steps of reasoning and action, but not the arguments an expert may consider during the problemsolving. Knowledge that can improve the quality of performance of CBR is therefore lost. The paper describes an approach that tackles this problem by representing arguments in a simple form and treating them, along with the traditional information contained in cases, as case properties. These properties are processed according to methods of numerical taxonomy when similarities between cases are being computed. The cases themselves are structured according to a model (CommonKADS) familiar in knowledge engineering but seldom applied to CBR and argumentation.

[1]  Henry Prakken,et al.  Towards a Formal Account of Reasoning about Evidence: Argumentation Schemes and Generalisations , 2003, Artificial Intelligence and Law.

[2]  Lawrence Magne Passport to World Band Radio , 1955 .

[3]  John Fox,et al.  Arguing about beliefs and actions , 1998, Applications of Uncertainty Formalisms.

[4]  Edwina L. Rissland,et al.  Arguments and cases: An inevitable intertwining , 1992, Artificial Intelligence and Law.

[5]  Bing Leng,et al.  An Engineering Approach for Troubleshooting Case Bases , 1997, ICCBR.

[6]  John A. Campbell Numerical Taxonomy: A Missing Link for Case-Based Reasoning and Autonomous Agents. , 2004 .

[7]  Miquel Sànchez-Marrè,et al.  CBR and Argument Schemes for Collaborative Decision Making , 2006, COMMA.

[8]  Kevin D. Ashley,et al.  Predicting outcomes of case based legal arguments , 2003, ICAIL.

[9]  Kenneth D. Forbus Qualitative Reasoning , 1997, The Computer Science and Engineering Handbook.

[10]  R. Sokal,et al.  Numerical Taxonomy: The Principles and Practice of Numerical Classification. , 1975 .

[11]  Stephen E. Toulmin,et al.  The Uses of Argument, Updated Edition , 2008 .

[12]  Vincent Aleven,et al.  Using background knowledge in case-based legal reasoning: A computational model and an intelligent learning environment , 2003, Artif. Intell..

[13]  S. Toulmin The uses of argument , 1960 .

[14]  Dimitris Papadias,et al.  Using Case-Based Reasoning for Argumentation with Multiple Viewpoints , 1997, ICCBR.

[15]  Kevin D. Ashley Reasoning with Cases and Hypotheticals in HYPO , 1991, Int. J. Man Mach. Stud..

[16]  James Popple,et al.  SHYSTER: A Pragmatic Legal Expert System , 1993 .

[17]  Guus Schreiber,et al.  Knowledge Engineering and Management: The CommonKADS Methodology , 1999 .

[18]  Anthony G. Cohn,et al.  Qualitative Reasoning , 1987, Advanced Topics in Artificial Intelligence.

[19]  Agnar Aamodt,et al.  Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..

[20]  PrakkenHenry,et al.  Towards a formal account of reasoning about evidence , 2003 .

[21]  C. Reed,et al.  Translating Toulmin Diagrams: Theory Neutrality in Argument Representation , 2005, Arguing on the Toulmin Model.