Case-Based Reasoning
- 1st Edition - September 1, 1993
- Imprint: Morgan Kaufmann
- Author: Janet Kolodner
- Language: English
- Paperback ISBN:9 7 8 - 1 - 5 5 8 6 0 - 2 3 7 - 3
- eBook ISBN:9 7 8 - 1 - 4 8 3 2 - 9 4 4 9 - 0
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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Request a sales quoteCase-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.
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
- 1.5.1 Range of Applicability and Real-World Usefulness1.5.2 Advantages and Disadvantages of CBR
- 1.6.1 Case-Based Reasoning and People
- 1.6.2 Building a Case-Based Reasoner
- 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
- 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
- 3.3.1 Justification and Adversarial Reasoning3.3.2 Classification and Interpretation3.3.3 Interpretive CBR and Problem Solving: Projection
- 3.4.1 Case-Based and Rule-Based Reasoning3.4.2 Case-Based and Model-Based Reasoning
- 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
- 4.4.1 CYRUS: A Model of Reconstructive Memory4.4.2 CELIA: A case-Based Approach to the Passage from Novice to Expert
- 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
- 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
- 5.3.1 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
- 5.4.1 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
- 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
- 6.2.1 Determining Coverage6.2.2 Methodologies for Choosing Index Vocabulary6.2.3 The Functional Methodology for Choosing Indexing Vocabulary
- 6.4.1 Specifying Content6.4.2 Specifying Context
- 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
- 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
- 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
- 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
- 9.2.1 Finding Correspondences9.2.2 Computing Degree of Similarity of Corresponding Features9.2.3 Weighting Dimensions of a Representation: Assigning Importance Values
- 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
- 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
- 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
- 11.2.1 Commonsense Transformation11.2.2 Model-Guided Repair
- 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
- 12.2.1 Choosing What Gets Adapted12.2.2 Finding an Appropriate Adaptation or Repair Strategy12.2.3 Choosing Between Several Adaptation Methods
- 12.3.1 Case-Based Adaptation12.3.2 Using Execution-Time Feedback12.3.3 Using Critics to Control Adaptation
- 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
- 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
- 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
- 15.3.1 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
- 15.4.1 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?
- 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
- Edition: 1
- Published: September 1, 1993
- Imprint: Morgan Kaufmann
- No. of pages: 612
- Language: English
- Paperback ISBN: 9781558602373
- eBook ISBN: 9781483294490
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