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Case-Based Reasoning

  • 1st Edition - September 1, 1993
  • Latest edition
  • Author: Janet Kolodner
  • Language: English

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 te… Read more

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Description

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 Reasoningby Janet Kolodner
    PrefacePart I Background
      1 What is Case-Based Reasoning?
        1.1 Introduction1.2 What Is a Case?1.3 Major CBR Issues: Composition and Specificity1.4 Processes and Issues
          1.4.1 Case Retrieval1.4.2 Proposing a Ballpark Solution1.4.3 Adaptation1.4.4 Evaluative Reasoning: Justification and Criticism1.4.5 Evaluative Testing1.4.6 Memory Update
        1.5 Applicability of Case-Based Reasoning
          1.5.1 Range of Applicability and Real-World Usefulness1.5.2 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 Readers1.8 Summary
        2 Case Studies of Several Case-Based Reasoners
          2.1 CHEF2.2 CASEY2.3 JULIA2.4 HYPO2.5 PROTOS2.6 CLAVIER2.7 Retrieval-Only Aiding and Advisory Systems
            2.7.1 A Hypothetical Architect's Assistant2.7.2 A Hypothetical Mediator's Assistant2.7.3. Some Real Aiding Systems
          2.8 Summary
        3 Reasoning Using Cases
          3.1 Case-Based Inference3.2 CBR and Problem Solving
            3.2.1 CBR for Planning3.2.2 CBR for Design3.2.3 CBR for Explanation and Diagnosis
          3.3 Interpretive CBR
            3.3.1 Justification and Adversarial Reasoning3.3.2 Classification and Interpretation3.3.3 Interpretive CBR and Problem Solving: Projection
          3.4 Case-Based and Other Reasoning Methods
            3.4.1 Case-Based and Rule-Based Reasoning3.4.2 Case-Based and Model-Based Reasoning
          3.5 Summary
        4 The Cognitive Model
          4.1 A Short Intellectual History4.2 Dynamic Memory
            4.2.1 Reminding4.2.2 MOPs4.2.3 TOPs4.2.4 Indexing4.2.5 Reminding Revisited
          4.3 Beyond Intentional Situations: Dynamic Memory and Model-Based Reasoning4.4 Some Running Cognitive Models
            4.4.1 CYRUS: A Model of Reconstructive Memory4.4.2 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 Knowledge4.5.2 Primary Processes4.5.3 Dynamic Memory and Learning4.5.4 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 Representations5.1.2 The Content of Solutions5.1.3 The Content of Case Outcomes
            5.2 The Issue of Case Presentation5.3 Case Studies
              5.3.1 MEDIATOR: Highly Structured Representations, Broad But Not Deep5.3.2 CASEY: Concentrating on Situation Description and Solution, Proposition-Based Representations5.3.3 CHEF: Representing a Solution Plan5.3.4 JULIA and KRITIK: Representing Design Cases, Concentrating on the Solution5.3.5 HYPO's Representations: Concentrating on Situation Description5.3.6 Formlike Representations
            5.4 Advanced Issues
              5.4.1 Grain Size of Cases: Monolithic Cases or Distributed Cases?5.4.2 Evolving Problem Descriptions5.4.3 Boundaries of Cases: Representing Cases in Continuous Environments
            5.5 Summary
          6 Indexing Vocabulary
            6.1 Qualities of Good Indexes6.1.1 Predictive Features6.1.2 Abstractness of Indexes6.1.3 Concreteness of Indexes6.1.4 Usefulness of Indexes
          6.2 Choosing Vocabulary
            6.2.1 Determining Coverage6.2.2 Methodologies for Choosing Index Vocabulary6.2.3 The Functional Methodology for Choosing Indexing Vocabulary
          6.3 Toward a Generally Applicable Indexing Vocabulary6.4 The Universal Index Frame: A Vocabulary for Intentional Situations
            6.4.1 Specifying Content6.4.2 Specifying Context
          6.5 Generally Applicable Indexing Schemes: Lessons Illustrated by the UIF
            6.5.1 Indexes Correspond to Interpretations of Situations6.5.2 Capturing Relationships Among Components of an Episode6.5.3 The Specificity of Indexes6.5.4 Surface Features and Abstract Features in Indexing and Reminding6.5.5 Modularity and Redundancy in an Indexing Scheme6.5.6 Describing Cases and Indexing Cases: The Differences
          6.6 Beyond the Universal Index Frame6.7 Summary
        7 Methods for Index Selection
          7.1 Choosing Indexes by Hand7.2 Choosing Indexes by Machine7.3 Choosing Indexes Based on a Checklist
            7.3.1 Difference-Based Indexing7.4 Difference-Based Indexing7.5 Combining Difference-Based and Checklist-Based Methods7.6 Explanation-Based Indexing
              7.6.1 Creating an Explanation7.6.2 Selecting Observable Features7.6.3 Generalization7.6.4 Dealing with Solution-Creation Goals7.6.5 Some Examples
            7.7 Combining Explanation-Based, Checklist-Based, and Difference-Based Methods7.8 Choosing an Automated Indexing Method7.9 Summary
        Part III Retrieving Cases from the Case Library
          8 Organizational Structures and Retrieval Algorithms
            8.1 A Note About Matching8.2 A Set of Cases8.3 Flat Memory, Serial Search8.4 Hierarchical Organizations of Cases: Shared Feature Networks8.5 Discrimination Networks8.6 A Major Disadvantage8.7 Redundant Discrimination Networks8.8 Flat Library, Parallel Search8.9 Hierarchical Memory, Parallel Search8.10 Discussion
              8.10.1 A Note on Parallelism8.10.2 Advantages of Hierarchical Organizations8.10.3 Integrating Search and Match Functions
            8.11 Summary
          9 Matching and Ranking Cases
            9.1 Some Definitions
              9.1.1 Dimensions, Descriptors, and Features9.1.2 Choosing What to Match9.1.3 Matching and Ranking9.1.4 Global and Local Matching Criteria: Taking Context into Account in Matching9.1.5 Absolute and Relative Matching and Ranking9.1.6 Input to Matching and Ranking Functions
            9.2 The Building Blocks of Matching and Ranking Process
              9.2.1 Finding Correspondences9.2.2 Computing Degree of Similarity of Corresponding Features9.2.3 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 Matching9.3.2 Adding Exclusion to the Ranking Procedure9.3.3 The Need to Take Context into Account in Ranking9.3.4 Making Ranking Dynamic Through Multiple Assignments of Importance9.3.5 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 Checklist10.1.2 During Search: Incremental Context Refinement
            10.2 Implementing Indexes10.3 Achieving Efficiency, Accuracy, and Flexibility10.4 Summary
        Part IV Using Cases
          11 Adaptation Methods and Strategies
            11.1 Substitution
              11.1.1 Reinstantiation11.1.2 Parameter Adjustment11.1.3 Local Search11.1.4 Query Memory11.1.5 Specialized Search11.1.6 Case-Based Substitution11.1.7 Memory Organization Requirements for Substitution Methods
            11.2 Transformation
              11.2.1 Commonsense Transformation11.2.2 Model-Guided Repair
            11.3 Special-Purpose Adaptation and Repair Heuristics11.4 Derivational Replay11.5 Summary
          12 Controlling Adaptation
            12.1 Identifying hat Needs to Be Fixed
              12.1.1 Using Differences Between Problem Specifications12.1.2 Using a Checklist12.1.3 Using Inconsistencies Between the Old Solution and Stated Goals12.1.4 Using Solution Projections12.1.5 Carrying Out a Solution and Analyzing Feedback12.1.6 Using Adaptation History: Compensatory Adaptation
            12.2 Choosing an Adaptation Strategy
              12.2.1 Choosing What Gets Adapted12.2.2 Finding an Appropriate Adaptation or Repair Strategy12.2.3 Choosing Between Several Adaptation Methods
            12.3 Choosing What Gets Adapted and the Method of Adaptation in Tandem
              12.3.1 Case-Based Adaptation12.3.2 Using Execution-Time Feedback12.3.3 Using Critics to Control Adaptation
            12.4 Flow of Control12.5 Summary
          13 Using Cases for Interpretation and Evaluation
            13.1 Exemplar-based Classification13.2 Case-Based Interpretation
              13.2.1 Analyzing and Retrieving Cases: Dimensions, Indexing, and the Case Analysis Record13.2.2 Positioning and Selecting Cases: The Claim Lattice13.2.3 Generating and Testing Arguments
            13.3 Critiquing Solutions: Case-Based Projection13.4 Summary
          14 Using Cases: Some Additional Issues
            14.1 Using Reasoning Goals to Guide Case-Based processes14.2 Anticipating Potential Problems and Opportunities for Enhancement14.3 Deriving Subgoals14.4 Types of Reasoning Goals and Tasks14.5 Goal Scheduling14.6 Integrating the Goal Scheduler With the Case-Based Reasoner14.7 When to use a Goal Scheduler14.8 A Neglected Complexity: Merging Pieces of Several Solutions14.9 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 Domain15.2.2 Generic Case-Based Reasoning Tasks15.2.3 Functions Cases Can Profitably Fulfill
            15.3 What Degree of Automation Should Be Used?
              15.3.1 Consideration 1: Required Creativity15.3.2 Consideration 2: Complexity of Evaluating Solutions and Effecting Repairs15.3.3 Consideration 3: Need to Consider Aesthetics, Values, and/or User Preferences15.3.4 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 Interaction15.7 Summary
              16 Conclusions, Opportunities, Challenges
                16.1 Case-Based Reasoning and Learning16.2 Conclusions16.3 Challenges and Opportunities
                  16.3.1 Knowledge Engineering Issues16.3.2 Scaleup: The Major Technological Issue16.3.3 Fundamental Issues and Enhanced Capabilities
                16.4 The Future
              Appendix: A Case Library of Case-Based Reasoning SystemsBibliographyIndex

Product details

  • Edition: 1
  • Latest edition
  • Published: June 28, 2014
  • Language: English

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