When Experience Is Wrong: Examining CBR for Changing Tasks and Environments

Case-based problem-solving systems reason and learn from experiences, building up case libraries of problems and solutions to guide future reasoning. The expected benefits of this learning process depend on two types of regularity: (1) problem-solution regularity, the relationship between problem-to-problem and solution-to-solution similarity measures that assures that solutions to similar prior problems are a useful starting point for solving similar current problems, and (2) problem-distribution regularity, the relationship between old and new problems that assures that the case library will contain cases similar to the new problems it encounters. Unfortunately, these types of regularity are not assured. Even in contexts for which initial regularity is sufficient, problems may arise if a system's users, tasks, or external environment change over time. This paper defines criteria for assessing the two types of regularity, discusses how the definitions may be used to assess the need for case-base maintenance, and suggests maintenance approaches for responding to those needs. In particular, it discusses the role of analysis of performance over time in responding to environmental changes.

[1]  Ralph Barletta,et al.  Building a case-based help desk application , 1993, IEEE Expert.

[2]  Boi Faltings.  Probabilistic Indexing for Case-Based Prediction , 1997, ICCBR.

[3]  R. Lathe.  Phd by thesis , 1988, Nature.

[4]  William Cheetham,et al.  Case-Based Reasoning in Color Matching , 1997, ICCBR.

[5]  David B. Leake,et al.  Using Introspective Reasoning to Refine Indexing , 1995, IJCAI.

[6]  David Leake,et al.  Case-Based Reasoning: Experiences, Lessons and Future Directions , 1996 .

[7]  Mark T. Keane,et al.  Design à la Déjà Vu Reducing the Adaptation Overhead , 1996 .

[8]  Barry Smyth,et al.  Modelling the Competence of Case-Bases , 1998, EWCBR.

[9]  Claude Sammut,et al.  Learning in Time Ordered Domains with Hidden Changes in Context , 2000 .

[10]  David B. Leake,et al.  Introspective learning for case-based planning , 1996 .

[11]  Barry Smyth,et al.  Advances in Case-Based Reasoning , 1996, Lecture Notes in Computer Science.

[12]  Houman Talebzadeh,et al.  Countrywide Loan-Underwriting Expert System , 1995, AI Mag..

[13]  Manuela M. Veloso,et al.  Planning and Learning by Analogical Reasoning , 1994, Lecture Notes in Computer Science.

[14]  Michael Albert Redmond,et al.  Learning by observing and understanding expert problem-solving , 1992 .

[15]  David C. Wilson,et al.  Categorizing Case-Base Maintenance: Dimensions and Directions , 1998, EWCBR.

[16]  Carla E. Brodley,et al.  Approaches to Online Learning and Concept Drift for User Identification in Computer Security , 1998, KDD.

[17]  Barry Smyth,et al.  Remembering To Forget: A Competence-Preserving Case Deletion Policy for Case-Based Reasoning Systems , 1995, IJCAI.

[18]  Ian D. Watson,et al.  Applying case-based reasoning - techniques for the enterprise systems , 1997 .

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