Case-Based Reasoning

Case-Based Reasoning

1st Edition - September 1, 1993

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  • Author: Janet Kolodner
  • eBook ISBN: 9781483294490

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Case-based reasoning is one of the fastest growing areas in the field of knowledge-based systems and this book, authored by a leader in the field, is the first comprehensive text on the subject. Case-based reasoning systems are systems that store information about situations in their memory. As new problems arise, similar situations are searched out to help solve these problems. Problems are understood and inferences are made by finding the closest cases in memory, comparing and contrasting the problem with those cases, making inferences based on those comparisons, and asking questions when inferences can't be made.This book presents the state of the art in case-based reasoning. The author synthesizes and analyzes a broad range of approaches, with special emphasis on applying case-based reasoning to complex real-world problem-solving tasks such as medical diagnosis, design, conflict resolution, and planning. The author's approach combines cognitive science and engineering, and is based on analysis of both expert and common-sense tasks. Guidelines for building case-based expert systems are provided, such as how to represent knowledge in cases, how to index cases for accessibility, how to implement retrieval processes for efficiency, and how to adapt old solutions to fit new situations. This book is an excellent text for courses and tutorials on case-based reasoning. It is also a useful resource for computer professionals and cognitive scientists interested in learning more about this fast-growing field.

Table of Contents

  • Case-Based Reasoning

    by Janet Kolodner


      Part I Background
        1 What is Case-Based Reasoning?
          1.1 Introduction

          1.2 What Is a Case?

          1.3 Major CBR Issues: Composition and Specificity

          1.4 Processes and Issues
            1.4.1 Case Retrieval

            1.4.2 Proposing a Ballpark Solution

            1.4.3 Adaptation

            1.4.4 Evaluative Reasoning: Justification and Criticism

            1.4.5 Evaluative Testing

            1.4.6 Memory Update

          1.5 Applicability of Case-Based Reasoning
            1.5.1 Range of Applicability and Real-World Usefulness

            1.5.2 Advantages and Disadvantages of CBR

          1.6 Cognitive Model, or Methodology for Building Expert Systems?
            1.6.1 Case-Based Reasoning and People
              1.6.2 Building a Case-Based Reasoner

            1.7 A Note to Readers

            1.8 Summary

          2 Case Studies of Several Case-Based Reasoners
            2.1 CHEF

            2.2 CASEY

            2.3 JULIA

            2.4 HYPO

            2.5 PROTOS

            2.6 CLAVIER

            2.7 Retrieval-Only Aiding and Advisory Systems
              2.7.1 A Hypothetical Architect's Assistant

              2.7.2 A Hypothetical Mediator's Assistant

              2.7.3. Some Real Aiding Systems

            2.8 Summary

          3 Reasoning Using Cases
            3.1 Case-Based Inference

            3.2 CBR and Problem Solving
              3.2.1 CBR for Planning

              3.2.2 CBR for Design

              3.2.3 CBR for Explanation and Diagnosis

            3.3 Interpretive CBR
              3.3.1 Justification and Adversarial Reasoning

              3.3.2 Classification and Interpretation

              3.3.3 Interpretive CBR and Problem Solving: Projection

            3.4 Case-Based and Other Reasoning Methods
              3.4.1 Case-Based and Rule-Based Reasoning

              3.4.2 Case-Based and Model-Based Reasoning

            3.5 Summary

          4 The Cognitive Model
            4.1 A Short Intellectual History

            4.2 Dynamic Memory
              4.2.1 Reminding

              4.2.2 MOPs

              4.2.3 TOPs

              4.2.4 Indexing

              4.2.5 Reminding Revisited

            4.3 Beyond Intentional Situations: Dynamic Memory and Model-Based Reasoning

            4.4 Some Running Cognitive Models
              4.4.1 CYRUS: A Model of Reconstructive Memory

              4.4.2 CELIA: A case-Based Approach to the Passage from Novice to Expert

            4.5 Summary of Claims
              4.5.1 The Structure and Organization of Knowledge

              4.5.2 Primary Processes

              4.5.3 Dynamic Memory and Learning

              4.5.4 The Structure and Role of General Knowledge

            4.6 Evidence of Case-Based Reasoning in People and Its Implications

          Part II The Case Library: Representing and Indexing Cases
            5 Representing Cases
              5.1 Components Parts of Cases
                5.1.1 The Content of Problem Representations

                5.1.2 The Content of Solutions

                5.1.3 The Content of Case Outcomes

              5.2 The Issue of Case Presentation

              5.3 Case Studies
                5.3.1 MEDIATOR: Highly Structured Representations, Broad But Not Deep

                5.3.2 CASEY: Concentrating on Situation Description and Solution, Proposition-Based Representations

                5.3.3 CHEF: Representing a Solution Plan

                5.3.4 JULIA and KRITIK: Representing Design Cases, Concentrating on the Solution

                5.3.5 HYPO's Representations: Concentrating on Situation Description

                5.3.6 Formlike Representations

              5.4 Advanced Issues
                5.4.1 Grain Size of Cases: Monolithic Cases or Distributed Cases?

                5.4.2 Evolving Problem Descriptions

                5.4.3 Boundaries of Cases: Representing Cases in Continuous Environments

              5.5 Summary

            6 Indexing Vocabulary
              6.1 Qualities of Good Indexes

              6.1.1 Predictive Features

              6.1.2 Abstractness of Indexes

              6.1.3 Concreteness of Indexes

              6.1.4 Usefulness of Indexes

            6.2 Choosing Vocabulary
              6.2.1 Determining Coverage

              6.2.2 Methodologies for Choosing Index Vocabulary

              6.2.3 The Functional Methodology for Choosing Indexing Vocabulary

            6.3 Toward a Generally Applicable Indexing Vocabulary

            6.4 The Universal Index Frame: A Vocabulary for Intentional Situations
              6.4.1 Specifying Content

              6.4.2 Specifying Context

            6.5 Generally Applicable Indexing Schemes: Lessons Illustrated by the UIF
              6.5.1 Indexes Correspond to Interpretations of Situations

              6.5.2 Capturing Relationships Among Components of an Episode

              6.5.3 The Specificity of Indexes

              6.5.4 Surface Features and Abstract Features in Indexing and Reminding

              6.5.5 Modularity and Redundancy in an Indexing Scheme

              6.5.6 Describing Cases and Indexing Cases: The Differences

            6.6 Beyond the Universal Index Frame

            6.7 Summary

          7 Methods for Index Selection
            7.1 Choosing Indexes by Hand

            7.2 Choosing Indexes by Machine

            7.3 Choosing Indexes Based on a Checklist
              7.3.1 Difference-Based Indexing
              7.4 Difference-Based Indexing

              7.5 Combining Difference-Based and Checklist-Based Methods

              7.6 Explanation-Based Indexing
                7.6.1 Creating an Explanation

                7.6.2 Selecting Observable Features

                7.6.3 Generalization

                7.6.4 Dealing with Solution-Creation Goals

                7.6.5 Some Examples

              7.7 Combining Explanation-Based, Checklist-Based, and Difference-Based Methods

              7.8 Choosing an Automated Indexing Method

              7.9 Summary

          Part III Retrieving Cases from the Case Library
            8 Organizational Structures and Retrieval Algorithms
              8.1 A Note About Matching

              8.2 A Set of Cases

              8.3 Flat Memory, Serial Search

              8.4 Hierarchical Organizations of Cases: Shared Feature Networks

              8.5 Discrimination Networks

              8.6 A Major Disadvantage

              8.7 Redundant Discrimination Networks

              8.8 Flat Library, Parallel Search

              8.9 Hierarchical Memory, Parallel Search

              8.10 Discussion
                8.10.1 A Note on Parallelism

                8.10.2 Advantages of Hierarchical Organizations

                8.10.3 Integrating Search and Match Functions

              8.11 Summary

            9 Matching and Ranking Cases
              9.1 Some Definitions
                9.1.1 Dimensions, Descriptors, and Features

                9.1.2 Choosing What to Match

                9.1.3 Matching and Ranking

                9.1.4 Global and Local Matching Criteria: Taking Context into Account in Matching

                9.1.5 Absolute and Relative Matching and Ranking

                9.1.6 Input to Matching and Ranking Functions

              9.2 The Building Blocks of Matching and Ranking Process
                9.2.1 Finding Correspondences

                9.2.2 Computing Degree of Similarity of Corresponding Features

                9.2.3 Weighting Dimensions of a Representation: Assigning Importance Values

              9.3 Putting It All together
                9.3.1 Matching and Ranking Using a Numeric Function: Nearest-Neighbor Matching

                9.3.2 Adding Exclusion to the Ranking Procedure

                9.3.3 The Need to Take Context into Account in Ranking

                9.3.4 Making Ranking Dynamic Through Multiple Assignments of Importance

                9.3.5 Using Preferences to Implement a Relative Ranking Scheme

              9.4 Summary

            10 Indexing and Retrieval
              10.1 Situation Assessment: Choosing Indexes for Retrieval
                10.1.1 Before Search: Context Setting Using a Checklist

                10.1.2 During Search: Incremental Context Refinement

              10.2 Implementing Indexes

              10.3 Achieving Efficiency, Accuracy, and Flexibility

              10.4 Summary

          Part IV Using Cases
            11 Adaptation Methods and Strategies
              11.1 Substitution
                11.1.1 Reinstantiation

                11.1.2 Parameter Adjustment

                11.1.3 Local Search

                11.1.4 Query Memory

                11.1.5 Specialized Search

                11.1.6 Case-Based Substitution

                11.1.7 Memory Organization Requirements for Substitution Methods

              11.2 Transformation
                11.2.1 Commonsense Transformation

                11.2.2 Model-Guided Repair

              11.3 Special-Purpose Adaptation and Repair Heuristics

              11.4 Derivational Replay

              11.5 Summary

            12 Controlling Adaptation
              12.1 Identifying hat Needs to Be Fixed
                12.1.1 Using Differences Between Problem Specifications

                12.1.2 Using a Checklist

                12.1.3 Using Inconsistencies Between the Old Solution and Stated Goals

                12.1.4 Using Solution Projections

                12.1.5 Carrying Out a Solution and Analyzing Feedback

                12.1.6 Using Adaptation History: Compensatory Adaptation

              12.2 Choosing an Adaptation Strategy
                12.2.1 Choosing What Gets Adapted

                12.2.2 Finding an Appropriate Adaptation or Repair Strategy

                12.2.3 Choosing Between Several Adaptation Methods

              12.3 Choosing What Gets Adapted and the Method of Adaptation in Tandem
                12.3.1 Case-Based Adaptation

                12.3.2 Using Execution-Time Feedback

                12.3.3 Using Critics to Control Adaptation

              12.4 Flow of Control

              12.5 Summary

            13 Using Cases for Interpretation and Evaluation
              13.1 Exemplar-based Classification

              13.2 Case-Based Interpretation
                13.2.1 Analyzing and Retrieving Cases: Dimensions, Indexing, and the Case Analysis Record

                13.2.2 Positioning and Selecting Cases: The Claim Lattice

                13.2.3 Generating and Testing Arguments

              13.3 Critiquing Solutions: Case-Based Projection

              13.4 Summary

            14 Using Cases: Some Additional Issues
              14.1 Using Reasoning Goals to Guide Case-Based processes

              14.2 Anticipating Potential Problems and Opportunities for Enhancement

              14.3 Deriving Subgoals

              14.4 Types of Reasoning Goals and Tasks

              14.5 Goal Scheduling

              14.6 Integrating the Goal Scheduler With the Case-Based Reasoner

              14.7 When to use a Goal Scheduler

              14.8 A Neglected Complexity: Merging Pieces of Several Solutions

              14.9 Summary

          Part V Pulling It All Together
            15 Building a Case-Based Reasoner
              15.1 First things First: When Should a Case-Based Reasoner Be Used?

              15.2 Which Tasks and Subtasks Should the Case-Based Reasoner Support?
                15.2.1 Analysis of the Task Domain

                15.2.2 Generic Case-Based Reasoning Tasks

                15.2.3 Functions Cases Can Profitably Fulfill

              15.3 What Degree of Automation Should Be Used?
                15.3.1 Consideration 1: Required Creativity

                15.3.2 Consideration 2: Complexity of Evaluating Solutions and Effecting Repairs

                15.3.3 Consideration 3: Need to Consider Aesthetics, Values, and/or User Preferences

                15.3.4 Consideration 4: Locus of Complexity

              15.4 Building and Maintaining the Case Library
                15.4.1 Collecting Cases: Which Ones?

                15.4.2 Achieving Coverage and Reliability

                  15.5 Maintaining the Case Library
                    15.5.1 Collecting Cases: How?

                    15.5.2 Collecting Cases: What Constitutes a Case?

                  15.6 Case Presentation and Human-Computer Interaction

                  15.7 Summary

                16 Conclusions, Opportunities, Challenges
                  16.1 Case-Based Reasoning and Learning

                  16.2 Conclusions

                  16.3 Challenges and Opportunities
                    16.3.1 Knowledge Engineering Issues

                    16.3.2 Scaleup: The Major Technological Issue

                    16.3.3 Fundamental Issues and Enhanced Capabilities

                  16.4 The Future

                Appendix: A Case Library of Case-Based Reasoning Systems



Product details

  • No. of pages: 612
  • Language: English
  • Copyright: © Morgan Kaufmann 1993
  • Published: September 1, 1993
  • Imprint: Morgan Kaufmann
  • eBook ISBN: 9781483294490

About the Author

Janet Kolodner

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