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• Notation.
• -1. Introduction.1.1 Similarity and case-based reasoning.1.2 Objective of this book. 1.3 Overview.-
• 2. Similarity and Case-Based Inference. 2.1 Model-based and instance-based approaches. 2.2 Similarity-based methods. 2.4 Case-based inference. 2.5 Summary and remarks.-
• 3. Constraint-Based Modeling of Case-Based Inference. 3.1 Basic concepts. 3.2 Constraint-based inference. 3.3 Case-based approximation. 3.4 Learning similarity hypotheses. 3.5 Application to statistical inference. 3.6 Summary and remarks.-
• 4. Probabilistic Modeling of Case-Based Inference. 4.1 Basic probabilistic concepts. 4.2 Case-based inference, probabilistic reasoning, and statistical inference. 4.3 Learning probabilistic similarity hypotheses. 4.4 Experiments with regression and label ranking. 4.5 Case-based inference as evidential reasoning. 4.6 Assessment of cases. 4.7 Complex similarity hypotheses. 4.8 Approximate probabilistic inference. 4.9 Summary and remarks.
• -5. Fuzzy Set-Based Modeling of Case-Based Inference I. 5.1 Background on possibility theory . 5.2 Fuzzy rule-based modeling of the CBI hypothesis. 5.3 Generalized possibilistic. 5.4 Extensions of the basic model. 5.5 Experimental studies. 5.6 Calibration of CBI models. 5.7 Relations to other fields. 5.8 Summary and remarks. 6.1 Gradual inference rules. 6.2 Certainty rules. 6.3 Cases as information sources. 6.4 Exceptionality and assessment of cases. 6.5 Local rules. 6.6 Summary and remarks.-
• 7. Case-Based Decision Making. 7.1 Case-based decision theory. 7.2 Nearest Neighbor decisions. 7.4 Fuzzy quantification in act evaluation. 7.5 A CBI framework of CBDM. 7.6 CBDM models: A discussion of selected issues. 7.7 Experience-based decision making. 7.8 Summary and remarks.
• -8. Conclusions and Outlook A. Possibilistic Dominance in Qualitative Decisions.- References.
• (source: Nielsen Book Data)

• Intro; Preface; Organization; Abstracts of Invited Papers; Mapping the Challenges and Opportunities of CBR for eXplainable AI; Some Shades of Grey! Interpretability and Explanatory Capacity of Deep Neural Networks; Model-Based Reasoning for Explainable AI as a Service; Contents; Comparing Similarity Learning with Taxonomies and One-Mode Projection in Context of the FEATURE-TAK Framework; 1 Introduction; 2 Weighted One Mode Projection in FEATURE-TAK; 2.1 FEATURE-TAK; 2.2 Integration of the Weighted One-Mode Projection; 3 Evaluation; 3.1 Similarity Matrix Computation and Modelling Assumptions

• 3.2 Evaluation Results4 Discussion and Outlook; References; An Algorithm Independent Case-Based Explanation Approach for Recommender Systems Using Interaction Graphs; 1 Introduction; 2 Related Work; 3 Explanations Based on Interaction Graphs; 3.1 The Case-Based Explanation System; 3.2 Link Prediction Similarity Measures; 4 Evaluation; 4.1 Data; 4.2 Experimental Setup; 4.3 Results; 5 Conclusions and Future Work; References; Explanation of Recommenders Using Formal Concept Analysis; 1 Introduction; 2 Related Work; 3 Formal Concept Analysis; 4 FCA-Based Explanation Algorithm

• 4.1 Explanation of the User Profile4.2 Explaining a Recommendation; 5 Evaluation; 5.1 Global Behaviour of the FCA Lattices; 5.2 Item Selection Strategies; 6 Conclusions and Future Work; References; FLEA-CBR
• A Flexible Alternative to the Classic 4R Cycle of Case-Based Reasoning; 1 Introduction; 2 Related Work; 3 FLEA-CBR; 3.1 Problem Description; 3.2 Overview and Background; 3.3 Core Features; 3.4 Find; 3.5 Learn; 3.6 Explain; 3.7 Adapt; 4 Example Usages; 4.1 CBR and Creativity; 4.2 Library Service Optimization; 5 Conclusion and Future Work; References

• Lazy Learned Screening for Efficient Recruitment1 Introduction; 2 Related Work; 2.1 Existing Approaches to Screening; 2.2 Existing Semantic Resources; 3 Design and Implementation; 3.1 Case Representation; 3.2 Similarity Functions; 3.3 The CBR Cycle; 4 Test and Evaluation; 4.1 Setup; 4.2 Experiment 1; 4.3 Experiment 2; 5 Results and Discussion; 5.1 Experiment 1; 5.2 Experiment 2; 6 Conclusion and Future Work; References; On the Generalization Capabilities of Sharp Minima in Case-Based Reasoning; 1 Introduction; 2 Background and Related Work

• 2.1 Case Base Maintenance and Instance-Based Learning2.2 Sharp and Flat Minima of an Error Function; 3 Case Base Maintenance as Optimization Problem; 3.1 Case Base Editing Problem; 3.2 Introspective Problem-Solving Quality; 3.3 Local Optima in Case Base Editing; 3.4 Hill-Climbing Case Base Editors; 4 Sharpness of a Case Base Configuration; 4.1 Characterizing Flat and Sharp Case Base Editing Optima; 4.2 Discussion of the Sharpness Measure; 5 Empirical Evaluation; 5.1 Correlation Between Sharpness and Generalization; 5.2 Hill-Climber Variants and Their Optima; 6 Conclusion; References

This book constitutes the refereed proceedings of the 27th International Conference on Case-Based Reasoning Research and Development, ICCBR 2019, held in Otzenhausen, Germany, in September 2019. The 26 full papers presented in this book were carefully reviewed and selected from 43 submissions. 15 were selected for oral presentation and 11 for poster presentation. The theme of ICCBR 2019, "Explainable AI (XAI), " was highlighted by several activities. These papers, which are included in the proceedings, address many themes related to the theory and application of case-based reasoning and its future direction. -- Provided by publisher

• The book opens with a general introduction to CBR presenting the basic ideas and concepts, setting the terminology, and looking at CBR from some new points of view. The main part of the book, consisting of nine chapters, is devoted to detailed presentations of CBR applications successfully performed to various areas. Among these application areas are decision and sales support, text processing, adaptation, planning, design, software engineering, tutoring systems, and medicine. The remaining chapters present areas related to CBR, a glossary, a subject index and bibliography.
• (source: Nielsen Book Data)

This state-of-the-art survey presents a coherent summary of research and development in case-based reasoning (CBR) undertaken in Germany in recent years.The book opens with a general introduction to CBR presenting the basic ideas and concepts, setting the terminology, and looking at CBR from some new points of view. The main part of the book, consisting of nine chapters, is devoted to detailed presentations of CBR applications successfully performed in various areas. Among these application areas are decision and sales support, text processing, adaptation, planning, design, software engineering, tutoring systems, and medicine. The remaining chapters present areas related to CBR as well as a glossary, a subject index and bibliography.
(source: Nielsen Book Data)

• Innovations in Case-Based Reasoning Applications.- Case-Based Reasoning for Medical and Industrial Decision Support Systems.- Development of Industrial Knowledge Management Applications with Case-Based Reasoning.- Case Based Reasoning for Supporting Strategy Decision Making in Small and Medium Enterprises.- Heterogeneity in Ontological CBR Systems.- The Adaptation Problem in Medical Case-Based Reasoning Systems.- Prototype-Based Classification in Unbalanced Biomedical Problems.- Case-Based Ranking for Environmental Risk Assessment.- CookIIS - A Successful Recipe Advisor and Menu Creator.
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Case-based reasoning (CBR) is an Artificial Intelligence (AI) technique to support the capability of reasoning and learning in advanced decision support systems. CBR exploits the specific knowledge collected on previously encountered and solved situations, which are known as cases. In this book, we have collected a selection of papers on very recent CBR applications. These, after an in-depth analysis of their specific application domain needs, propose proper methodological solutions and give encouraging evaluation results, which have in some cases led to the commercialization step. The collected contributions demonstrate the capability of CBR to solve or handle issues which would be too difficult to manage with other classical AI methods and techniques, such as rules or models. The heterogeneity of the involved application domains indicates the flexibility of CBR, and its applicability in all those fields where experiential knowledge is (readily) available.
(source: Nielsen Book Data)

• Case-Based Reasoning and Expert Systems / Klaus-Dieter Althoff
• Reproducibility and Efficiency of Scientific Data Analysis: Scientific Workflows and Case-Based Reasoning / Yolanda Gil
• A Computer Aided System for Post-operative Pain Treatment Combining Knowledge Discovery and Case-Based Reasoning / Mobyen Uddin Ahmed and Peter Funk
• Developing Case-Based Reasoning Applications Using myCBR 3 / Kerstin Bach and Klaus-Dieter Althoff
• Diverse Plan Generation by Plan Adaptation and by First-Principles Planning: A Comparative Study / Alexandra Coman and Héctor Muñoz-Avila
• Case-Based Appraisal of Internet Domains / Sebastian Dieterle and Ralph Bergmann
• Harnessing the Experience Web to Support User-Generated Product Reviews / Ruihai Dong, Markus Schaal, Michael P. O'Mahony, Kevin McCarthy and Barry Smyth
• Adapting Spatial and Temporal Cases / Valmi Dufour-Lussier, Florence Le Ber, Jean Lieber and Laura Martin
• eCo: Managing a Library of Reusable Behaviours / Gonzalo Flórez-Puga, Guillermo Jiménez-Díaz and Pedro A. González-Calero.

• Toward Measuring the Similarity of Complex Event Sequences in Real-Time / Odd Erik Gundersen
• Case-Based Project Scheduling / Mario Gómez and Enric Plaza
• Adapting Numerical Representations of Lung Contours Using Case-Based Reasoning and Artificial Neural Networks / Julien Henriet, Pierre-Emmanuel Leni, Rémy Laurent, Ana Roxin and Brigitte Chebel-Morello, et al.
• Adaptation in a CBR-Based Solver Portfolio for the Satisfiability Problem / Barry Hurley and Barry O'Sullivan
• A Local Rule-Based Attribute Weighting Scheme for a Case-Based Reasoning System for Radiotherapy Treatment Planning / Rupa Jagannathan and Sanja Petrovic
• Learning and Reusing Goal-Specific Policies for Goal-Driven Autonomy / Ulit Jaidee, Héctor Muñoz-Avila and David W. Aha
• Custom Accessibility-Based CCBR Question Selection by Ongoing User Classification / Vahid Jalali and David Leake
• Feature Weighting and Confidence Based Prediction for Case Based Reasoning Systems / Debarun Kar, Sutanu Chakraborti and Balaraman Ravindran.

• A Case-Based Approach to Mutual Adaptation of Taxonomic Ontologies / Sergio Manzano, Santiago Ontañón and Enric Plaza
• A Lazy Learning Approach to Explaining Case-Based Reasoning Solutions / David McSherry
• Confidence in Workflow Adaptation / Mirjam Minor, Mohd. Siblee Islam and Pol Schumacher
• Retrieval and Clustering for Business Process Monitoring: Results and Improvements / Stefania Montani and Giorgio Leonardi
• A Case-Based Approach to Cross Domain Sentiment Classification / Bruno Ohana, Sarah Jane Delany and Brendan Tierney
• GENA: A Case-Based Approach to the Generation of Audio-Visual Narratives / Santiago Ontañón, Josep Lluís Arcos, Josep Puyol-Gruart, Eusebio Carasusán and Daniel Giribet, et al.
• On Knowledge Transfer in Case-Based Inference / Santiago Ontañón and Enric Plaza
• Case-Based Aggregation of Preferences for Group Recommenders / Lara Quijano-Sánchez, Derek Bridge, Belén Díaz-Agudo and Juan A. Recio-García.

• A Case-Based Solution to the Cold-Start Problem in Group Recommenders / Lara Quijano-Sánchez, Derek Bridge, Belén Díaz-Agudo and Juan A. Recio-García
• Opponent Type Adaptation for Case-Based Strategies in Adversarial Games / Jonathan Rubin and Ian Watson
• Exploiting Extended Search Sessions for Recommending Search Experiences in the Social Web / Zurina Saaya, Markus Schaal, Maurice Coyle, Peter Briggs and Barry Smyth
• Event Extraction for Reasoning with Text / Sadiq Sani, Nirmalie Wiratunga, Stewart Massie and Robert Lothian
• Explanation-Aware Design of Mobile myCBR-Based Applications / Christian Severin Sauer, Alexander Hundt and Thomas Roth-Berghofer
• A Competitive Measure to Assess the Similarity between Two Time Series / Joan Serrà and Josep Lluís Arcos
• Case-Based Reasoning Applied to Textile Industry Processes / Beatriz Sevilla Villanueva and Miquel Sànchez-Marrè
• Natural Language Generation through Case-Based Text Modification / Josep Valls and Santiago Ontañón
• Case-Based Reasoning for Turbine Trip Diagnostics / Aisha Yousuf and William Cheetham.

This book constitutes the thoroughly refereed post-conference proceedings of the 20th International Conference on Case-Based Reasoning Research and Development (ICCBR 2012) held in Lyon, France, September 3-6, 2012. The 34 revised full papers presented were carefully selected from 51 submissions. The presentations and posters covered a wide range of CBR topics of interest to both practitioners and researchers, including foundational issues covering case representation, similarity, retrieval, and adaptation; conversational CBR recommender systems; multi-agent collaborative systems; data mining; time series analysis; Web applications; knowledge management; legal reasoning; healthcare systems and planning and scheduling systems.
(source: Nielsen Book Data)

• First look
• What's happening elsewhere : Reasoning from a case to the world
• Reasoning from analogy
• Black boxes : Using cases to study input-output machines
• Complicating and combining black boxes : Where is the value in art?
• Imagining cases
• Where do you stop?
• IOUs, promissory notes, and killer questions : What about Mozart? What about murder?
• Last words.

In 1963, Howard S. Becker gave a lecture about deviance, challenging the then-conventional definition that deviance was inherently criminal and abnormal and arguing that instead, deviance was better understood as a function of labeling. At the end of his lecture, a distinguished colleague standing at the back of the room, puffing a cigar, looked at Becker quizzically and asked, "What about murder? Isn't that really deviant?" It sounded like Becker had been backed into a corner. Becker, however, wasn't defeated! Reasonable people, he countered, differ over whether certain killings are murder or justified homicide, and these differences vary depending on what kinds of people did the killing. In What About Mozart? What About Murder?, Becker uses this example, along with many others, to demonstrate the different ways to study society, one that uses carefully investigated, specific cases and another that relies on speculation and on what he calls "killer questions, " aimed at taking down an opponent by citing invented cases. Becker draws on a lifetime of sociological research and wisdom to show, in helpful detail, how to use a variety of kinds of cases to build sociological knowledge. With his trademark conversational flair and informal, personal perspective Becker provides a guide that researchers can use to produce general sociological knowledge through case studies. He champions research that has enough data to go beyond guesswork and urges researchers to avoid what he calls "skeleton cases, " which use fictional stories that pose as scientific evidence. Using his long career as a backdrop, Becker delivers a winning book that will surely change the way scholars in many fields approach their research.
(source: Nielsen Book Data)

• The Bites Eclectic: Critique-Based Conversational Recommendation for Diversity-Focused Meal Planning.- Evaluation of Similarity Measures for Flight Simulator Training Scenarios.- Instance-based Counterfactual Explanations for Time Series Classification. -User Evaluation to Measure the Perception of Similarities Measures in Artworks.- Measuring Financial Time Series Similarity With a View to Identifying Profitable Stock Market Opportunities.- A Case-Based Reasoning Approach to Predicting and Explaining Running Related Injuries.- Bayesian Feature Construction for Case-Based Reasoning: Generating Good Checklists.- Revisiting Fast and Slow Thinking in Case-Based Reasoning.- Harmonizing Case Retrieval and Adaptation with Alternating Optimization.- Adaptation knowledge discovery using positive and negative cases.- When Revision-Based Case Adaptation Meets Analogical Extrapolation.- Inferring Case-Based Reasoners' Knowledge to Enhance Interactivity.- A case-based approach for the selection of explanation algorithms in image classification.- Towards Richer Realizations of Holographic CBR. -Handling Climate Change Using Counterfactuals: Using Counterfactuals in Data Augmentation to Predict Crop Growth in an Uncertain Climate Future.- Case-Based Approach to Data-to-Text Generation. - On Combining Knowledge-Engineered and Network-Extracted Features for Retrieval.- Task and Situation Structures for Case-based Planning.- Learning Adaptations for Case-Based Classification: A Neural Network Approach.- Similar Questions Correspond to Similar SQL Queries: A Case-Based Reasoning Approach for Text-to-SQL Translation.- Deciphering Ancient Chinese Oracle Bone Inscriptions using Case-Based Reasoning.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 29th International Conference on Case-Based Reasoning, ICCBR 2021, which took place in Salamanca, Spain, during September 13-16, 2021. The 21 papers presented in this volume were carefully reviewed and selected from 85 submissions. They deal with AI and related research focusing on comparison and integration of CBR with other AI methods such as deep learning architectures, reinforcement learning, lifelong learning, and eXplainable AI (XAI).
(source: Nielsen Book Data)

• FOREWORD.PREFACE.ABOUT THE AUTHORS.1 INTRODUCTION.1.1 Background.1.2 Components and Features of Case-Based Reasoning.1.2.1 CBR System versus Rule-Based System.1.2.2 CBR versus Human Reasoning.1.2.3 CBR Life Cycle.1.3 Guidelines for the Use of Case-Based Reasoning.1.4 Advantages of Using Case-Based Reasoning.1.5 Case Representation and Indexing.1.5.1 Case Representation.1.5.2 Case Indexing.1.6 Case Retrieval.1.7 Case Adaptation.1.8 Case Learning and Case-Base Maintenance.1.8.1 Learning in CBR Systems.1.8.2 Case-Base Maintenance.1.9 Example of Building a Case-Based Reasoning System.1.9.1 Case Representation.1.9.2 Case Indexing.1.9.3 Case Retrieval.1.9.4 Case Adaptation.1.9.5 Case-Base Maintenance.1.10 Case-Based Reasoning: Methodology or Technology?1.11 Soft Case-Based Reasoning.1.11.1 Fuzzy Logic.1.11.2 Neural Networks.1.11.3 Genetic Algorithms.1.11.4 Some CBR Tasks for Soft Computing Applications.1.12 Summary.References.2 CASE REPRESENTATION AND INDEXING.2.1 Introduction.2.2 Traditional Methods of Case Representation.2.2.1 Relational Representation.2.2.2 Object-Oriented Representation.2.2.3 Predicate Representation.2.2.4 Comparison of Case Representations.2.3 Soft Computing Techniques for Case Representation.2.3.1 Case Knowledge Representation Based on Fuzzy Sets.2.3.2 Rough Sets and Determining Reducts.2.3.3 Prototypical Case Generation Using Reducts with Fuzzy Representation.2.4 Case Indexing.2.4.1 Traditional Indexing Method.2.4.2 Case Indexing Using a Bayesian Model.2.4.3 Case Indexing Using a Prototype-Based Neural Network.2.4.4 Case Indexing Using a Three-Layered Back Propagation Neural Network.2.5 Summary.References.3 CASE SELECTION AND RETRIEVAL.3.1 Introduction.3.2 Similarity Concept.3.2.1 Weighted Euclidean Distance.3.2.2 Hamming and Levenshtein Distances.3.2.3 Cosine Coefficient for Text-Based Cases.3.2.4 Other Similarity Measures.3.2.5 k-Nearest Neighbor Principle.3.3 Concept of Fuzzy Sets in Measuring Similarity.3.3.1 Relevance of Fuzzy Similarity in Case Matching.3.3.2 Computing Fuzzy Similarity Between Cases.3.4 Fuzzy Classification and Clustering of Cases.3.4.1 Weighted Intracluster and Intercluster Similarity.3.4.2 Fuzzy ID3 Algorithm for Classification.3.4.3 Fuzzy c-Means Algorithm for Clustering.3.5 Case Feature Weighting.3.5.1 Using Gradient-Descent Technique and Neural Networks.3.5.2 Using Genetic Algorithms.3.6 Case Selection and Retrieval Using Neural Networks.3.6.1 Methodology.3.6.2 Glass Identification.3.7 Case Selection Using a Neuro-Fuzzy Model.3.7.1 Selection of Cases and Class Representation.3.7.2 Formulation of the Network.3.8 Case Selection Using Rough-Self Organizing Map.3.8.1 Pattern Indiscernibility and Fuzzy Discretization of Feature Space.3.8.2 Methodology for Generation of Reducts.3.8.3 Rough SOM.3.8.4 Experimental Results.3.9 Summary.References.4 CASE ADAPTATION.4.1 Introduction.4.2 Traditional Case Adaptation Strategies.4.2.1 Reinstantiation.4.2.2 Substitution.4.2.3 Transformation.4.2.4 Example of Adaptation Knowledge in Pseudocode.4.3 Some Case Adaptation Methods.4.3.1 Learning Adaptation Cases.4.3.2 Integrating Rule- and Case-Based Adaptation Approaches.4.3.3 Using an Adaptation Matrix.4.3.4 Using Configuration Techniques.4.4 Case Adaptation Through Machine Learning.4.4.1 Fuzzy Decision Tree.4.4.2 Back-Propagation Neural Network.4.4.3 Bayesian Model.4.4.4 Support Vector Machine.4.4.5 Genetic Algorithms.4.5 Summary.References.5 CASE-BASE MAINTENANCE.5.1 Introduction.5.2 Background.5.3 Types of Case-Base Maintenance.5.3.1 Qualitative Maintenance.5.3.2 Quantitative Maintenance.5.4 Case-Base Maintenance Using a Rough-Fuzzy Approach.5.4.1 Maintaining the Client Case Base.5.4.2 Experimental Results.5.4.3 Complexity Issues.5.5 Case-Base Maintenance Using a Fuzzy Integral Approach.5.5.1 Fuzzy Measures and Fuzzy Integrals.5.5.2 Case-Base Competence.5.5.3 Fuzzy Integral-Based Competence Model.5.5.4 Experiment Results.5.6 Summary.References.6 APPLICATIONS.6.1 Introduction.6.2 Web Mining.6.2.1 Case Representation Using Fuzzy Sets.6.2.2 Mining Fuzzy Association Rules.6.3 Medical Diagnosis.6.3.1 System Architecture.6.3.2 Case Retrieval Using a Fuzzy Neural Network.6.3.3 Case Evaluation and Adaptation Using Induction.6.4 Weather Prediction.6.4.1 Structure of the Hybrid CBR System.6.4.2 Case Adaptation Using ANN.6.5 Legal Inference.6.5.1 Fuzzy Logic in Case Representation.6.5.2 Fuzzy Similarity in Case Retrieval and Inference.6.6 Property Valuation.6.6.1 PROFIT System.6.6.2 Fuzzy Preference in Case Retrieval.6.7 Corporate Bond Rating.6.7.1 Structure of a Hybrid CBR System Using Gas.6.7.2 GA in Case Indexing and Retrieval.6.8 Color Matching.6.8.1 Structure of the Color-Matching Process.6.8.2 Fuzzy Case Retrieval.6.9 Shoe Design.6.9.1 Feature Representation.6.9.2 Neural Networks in Retrieval.6.10 Other Applications.6.11 Summary.References.APPENDIXES.A FUZZY LOGIC.A.1 Fuzzy Subsets.A.2 Membership Functions.A.3 Operations on Fuzzy Subsets.A.4 Measure of Fuzziness.A.5 Fuzzy Rules.A.5.1 Definition.A.5.2 Fuzzy Rules for Classification.References.B ARTIFICIAL NEURAL NETWORKS.B.1 Architecture of Artificial Neural Networks.B.2 Training of Artificial Neural Networks.B.3 ANN Models.B.3.1 Single-Layered Perceptron.B.3.2 Multilayered Perceptron Using a Back-Propagation Algorithm.B.3.3 Radial Basis Function Network.B.3.4 Kohonen Neural Network.References.C GENETIC ALGORITHMS.C.1 Basic Principles.C.2 Standard Genetic Algorithm.C.3 Examples.C.3.1 Function Maximization.C.3.2 Traveling Salesman Problem.References.D ROUGH SETS.D.1 Information Systems.D.2 Indiscernibility Relation.D.3 Set Approximations.D.4 Rough Membership.D.5 Dependency of Attributes.References.INDEX.
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Provides a self-contained description of this important aspect of information processing and decision support technology. Presents basic definitions, principles, applications, and a detailed bibliography. Covers a range of real-world examples including control, data mining, and pattern recognition.
(source: Nielsen Book Data)

• Preface.- Fuzzy Sets.- Artificial Neural Networks.- Genetic Algorithms.- Neuro-Fuzzy Computing.- Applications.The complete table of contents can be found on the Internet: http://www.springer.de.
• (source: Nielsen Book Data)

Soft Computing in Case Based Reasoning demonstrates how various soft computing tools can be applied to design and develop methodologies and systems with case based reasoning for real-life decision-making or recognition problems.Comprising contributions from experts from all over the world, it:- Provides an introduction to CBR and soft computing, and the relevance of their integration- Evaluates the strengths and weaknesses of CBR in its current form- Presents recent developments and significant applications in domains such as data-mining, medical diagnosis, knowledge-based expert systems, banking, and forensic investigation- Addresses new information on developing intelligent systemsThis book will be of particular interest to graduate students and researchers in computer science, electrical engineering and information technology but it will also be of interest to researchers and practitioners in the fields of systems design, pattern recognition and data mining.
(source: Nielsen Book Data)

• Network Management and Problem-Solving
• Problem-Solving with Expert Systems
• The Case-Based Reasoning Approach to Problem-Solving
• Examples of Successful Case-Based Applications. Case-Based Reasoning in Network Fault Management Systems
• Implementing Case-Based Reasoning in Fault Management Systems.
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This work is aimed at network software developers, project managers, engineers and researchers, but should also be useful for network adminstrators, support staff, AI professionals and graduate students on network management courses. Offering an alternative to the expert systems paradigm for automating network management tasks, which will solve a problem but will not store the solution for the future, case based reasoning enables the system to retrieve the solution and attempt to adapt it to solve a new problem. This work discusses the applications of CBR-based network management, and provides instructions for building CBR-based network solvers. Examples of successful CB applications are included.
(source: Nielsen Book Data)

This monograph presents aspects of research into the application of artificial intelligence in structural design. The authors, leaders in the development of Case Based Design techniques applied to structural engineering provide a detailed insight into their state-of-the-art research on the implementation of novel reasoning techniques in structural design. Case Based Design enables computers not only to store, retrieve and present past designs but also to synthesize design solutions. The primary aim of this book is to address the issues of representation, indexing, retrieval and adaptation in Case Based Design. The issues are illustrated by examples in conceptual structural design.
(source: Nielsen Book Data)

• Explainability in CBR Using Case-based Reasoning for Capturing Expert Knowledge on Explanation Methods.- A Few Good Counterfactuals: Generating Interpretable, Plausible and Diverse Counterfactual Explanations.- How close is too close? The Role of Feature Attributions in Discovering Counterfactual Explanations.- Algorithmic Bias and Fairness in Case-Based Reasoning.- "Better" Counterfactuals, Ones People Can Understand: Psychologically-Plausible Case-Based Counterfactuals Using Categorical Features for Explainable AI (XAI).- Representation and Similarity Extracting Case Indices from Convolutional Neural Networks: A Comparative Study.- Exploring the Effect of Recipe Representation on Critique-based Conversational Recommendation.- Explaining CBR Systems Through Retrieval and Similarity Measure Visualizations: A Case Study.- Adapting Semantic Similarity Methods for Case-Based Reasoning in the Cloud.- Adaptation and Analogical Reasoning Case Adaptation with Neural Networks: Capabilities and Limitations.- A Deep Learning Approach to Solving Morphological Analogies.- Theoretical and Experimental Study of a Complexity Measure for Analogical Transfer.- Graphs and Optimisation Case-Based Learning and Reasoning Using Layered Boundary Multigraphs.- swarm optimization in small case bases for software effort estimation.- MicroCBR: Case-based Reasoning on Spatio-temporal Fault Knowledge Graph for Microservices Troubleshooting.- GPU-Based Graph Matching for Accelerating Similarity Assessment in Process-Oriented Case-Based Reasoning.- Never judge a case by its (unreliable) neighbors: Estimating Case Reliability for CBR.- CBR and Neural Networks Improving Automated Hyperparameter Optimization with Case-Based Reasoning.- A factorial study of neural network learning from differences for regression.- ase-Based Inverse Reinforcement Learning Using Temporal Coherence.- Analogy-based post-treatment of CNN image segmentations.- Case-Based Applications An Extended Case-Based Reasoning Approach to Race-Time Prediction in Recreational Marathon Runners.- Forecasting for Sustainable Dairy Produce: Enhanced Long-Term, Milk-Supply Forecasting Using k-NN for Data Augmentation, with Prefactual Explanations for XAI.- A Case-Based Approach for Content Planning in Data-to-Text Generation.- The use of computer-assisted Case-Based Reasoning to support clinical decision-making - a scoping review.
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This book constitutes the proceedings of the 30th International Conference on Case-Based Reasoning, ICCBR 2022, which took place in Nancy, France, during September 12-15, 2022.The theme of ICCBR 2022 was Global Challenges for CBR aiming to consider how CBR can and might contribute to challenges in sustainability, climate change, and global health. The 26 papers presented in this volume were carefully reviewed and selected from 68 submissions. They deal with AI and related research focusing on comparison and integration of CBR with other AI methods such as deep learning architectures, reinforcement learning, lifelong learning, and eXplainable AI (XAI).
(source: Nielsen Book Data)

• Preface
• Case-Based Reasoning Integrations: Approaches & Applications
• Applying Improved Case Indexing & Retrieving Using Ex-Post Information in Corporate Bankruptcy Prediction
• Case-Based Reasoning: History, Methodology & Development Trends
• A Temporal Case-Based Procedure for Cancellation Forecasting: A Case Study
• Provision of Safety for Technological Systems with the Aid of Case-Based Reasoning
• Mathematizing the Case-Based Reasoning Process
• Prototype-Based Reasoning for Diagnosis of Dysmorphic Syndromes
• Fuzzy Sets in Case-Based Reasoning
• New Approach of Case-Based Reasoning
• Index.
• (source: Nielsen Book Data)

Case-based reasoning (CBR), is the process of solving new problems based on the solutions of similar past problems. An auto mechanic who fixes an engine by recalling another car that exhibited similar symptoms is using case-based reasoning. A lawyer who advocates a particular outcome in a trial based on legal precedents and a physician, who considers the diagnosis and treatment of a previous patient having similar symptoms to determine the disease and treatment for the new patient, are using CBR; a prominent kind of analogy making. This book presents current research in the field of CBR including business predication researches of corporate failure using CBR, and mathematising the Case-Based Reasoning process.
(source: Nielsen Book Data)

This text describes projects in which case-based reasoning (CBR) is the focus for the representation and reasoning in a particular design domain. It provides a broad spectrum of applications and issues in applying and extending the concept of CBR to design.
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Using a case-based approach, this volume focuses on constructing explanations. All chapters relate to the problem of building computer programs that can develop hypotheses about what might have caused an observed event, an ability that is a hallmark of human intelligence.
(source: Nielsen Book Data)