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Advances in case-based reasoning : 8th European conference, ECCBR 2006, Fethiye, Turkey, September 4-7, 2006 ; proceedings / / Thomas R. Roth-Berghofer, Mehmet H. Goker, H. Altay Guvenir (eds.)
| Advances in case-based reasoning : 8th European conference, ECCBR 2006, Fethiye, Turkey, September 4-7, 2006 ; proceedings / / Thomas R. Roth-Berghofer, Mehmet H. Goker, H. Altay Guvenir (eds.) |
| Edizione | [1st ed. 2006.] |
| Pubbl/distr/stampa | Berlin, : Springer, 2006 |
| Descrizione fisica | 1 online resource (XIV, 570 p.) |
| Disciplina | 006.3/3 |
| Altri autori (Persone) |
Roth-BerghoferThomas R
GokerMehmet H GuvenirH. Altay <1957-> (Halil Altay) |
| Collana |
LNCS sublibrary. SL 7, Artificial intelligence
Lecture notes in computer science,Lecture notes in artificial intelligence |
| Soggetto topico |
Expert systems (Computer science)
Case-based reasoning |
| ISBN | 3-540-36846-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Invited Talks -- The Fun Begins with Retrieval: Explanation and CBR -- Completeness Criteria for Retrieval in Recommender Systems -- Is Consideration of Background Knowledge in Data Driven Solutions Possible at All? -- Reality Meets Research -- Research Papers -- Multi-agent Case-Based Reasoning for Cooperative Reinforcement Learners -- Retrieving and Reusing Game Plays for Robot Soccer -- Self-organising Hierarchical Retrieval in a Case-Agent System -- COBRAS: Cooperative CBR System for Bibliographical Reference Recommendation -- A Knowledge-Light Approach to Regression Using Case-Based Reasoning -- Case-Base Maintenance for CCBR-Based Process Evolution -- Evaluating CBR Systems Using Different Data Sources: A Case Study -- Decision Diagrams: Fast and Flexible Support for Case Retrieval and Recommendation -- Case-Based Reasoning for Knowledge-Intensive Template Selection During Text Generation -- Rough Set Feature Selection Algorithms for Textual Case-Based Classification -- Experience Management with Case-Based Assistant Systems -- The Needs of the Many: A Case-Based Group Recommender System -- Contextualised Ambient Intelligence Through Case-Based Reasoning -- Improving Annotation in the Semantic Web and Case Authoring in Textual CBR -- Unsupervised Case Memory Organization: Analysing Computational Time and Soft Computing Capabilities -- Further Experiments in Case-Based Collaborative Web Search -- Finding Similar Deductive Consequences – A New Search-Based Framework for Unified Reasoning from Cases and General Knowledge -- Case-Based Sequential Ordering of Songs for Playlist Recommendation -- A Comparative Study of Catalogue-Based Classification -- Ontology-Driven Development of Conversational CBR Systems -- Complexity Profiling for Informed Case-Base Editing -- Unsupervised Feature Selection for Text Data -- Combining Case-Based and Similarity-Based Product Recommendation -- On the Use of Selective Ensembles for Relevance Classification in Case-Based Web Search -- What Evaluation Criteria Are Right for CCBR? Considering Rank Quality -- Fast Case Retrieval Nets for Textual Data -- Combining Multiple Similarity Metrics Using a Multicriteria Approach -- Case Factory – Maintaining Experience to Learn -- Retrieval over Conceptual Structures -- An Analysis on Transformational Analogy: General Framework and Complexity -- Discovering Knowledge About Key Sequences for Indexing Time Series Cases in Medical Applications -- Application Papers -- Case-Based Reasoning for Autonomous Service Failure Diagnosis and Remediation in Software Systems -- Tracking Concept Drift at Feature Selection Stage in SpamHunting: An Anti-spam Instance-Based Reasoning System -- Case-Based Support for Collaborative Business -- A CBR-Based Approach for Supporting Consulting Agencies in Successfully Accompanying a Customer’s Introduction of Knowledge Management -- The PwC Connection Machine: An Adaptive Expertise Provider. |
| Altri titoli varianti | ECCBR 2006 |
| Record Nr. | UNINA-9910484973403321 |
| Berlin, : Springer, 2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in case-based reasoning : 6th European conference, ECCBR 2002, Aberdeen, Scotland, UK, September 4-7, 2002, proceedings / / edited by Susan Craw, Alun Preece
| Advances in case-based reasoning : 6th European conference, ECCBR 2002, Aberdeen, Scotland, UK, September 4-7, 2002, proceedings / / edited by Susan Craw, Alun Preece |
| Edizione | [1st ed. 2002.] |
| Pubbl/distr/stampa | Berlin, Germany ; ; New York, New York : , : Springer, , [2002] |
| Descrizione fisica | 1 online resource (XII, 656 p.) |
| Disciplina | 006.33 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Expert systems (Computer science)
Case-based reasoning |
| ISBN | 3-540-46119-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Invited Papers -- Integrating Background Knowledge into Nearest-Neighbor Text Classification -- Applying Knowledge Management: Techniques for Building Organisational Memories -- Research Papers -- On the Complexity of Plan Adaptation by Derivational Analogy in a Universal Classical Planning Framework -- Inductive Learning for Case-Based Diagnosis with Multiple Faults -- Diverse Product Recommendations Using an Expressive Language for Case Retrieval -- Digital Image Similarity for Geo-spatial Knowledge Management -- Poetry Generation in COLIBRI -- Adaptation Using Iterated Estimations -- The Use of a Uniform Declarative Model in 3D Visualisation for Case-Based Reasoning -- Experiments on Case-Based Retrieval of Software Designs -- Exploiting Taxonomic and Causal Relations in Conversational Case Retrieval -- Bayesian Case Reconstruction -- Relations between Customer Requirements, Performance Measures, and General Case Properties for Case Base Maintenance -- Representing Temporal Knowledge for Case-Based Prediction -- Local Predictions for Case-Based Plan Recognition -- Automatically Selecting Strategies for Multi-Case-Base Reasoning -- Diversity-Conscious Retrieval -- Improving Case Representation and Case Base Maintenance in Recommender Agents -- Similarity Assessment for Generalizied Cases by Optimization Methods -- Case Acquisition in a Project Planning Environment -- Improving Case-Based Recommendation -- Efficient Similarity Determination and Case Construction Techniques for Case-Based Reasoning -- Constructive Adaptation -- A Fuzzy Case Retrieval Approach Based on SQL for Implementing Electronic Catalogs -- Integrating Hybrid Rule-Based with Case-Based Reasoning -- Search and Adaptation in a Fuzzy Object Oriented Case Base -- Deleting and Building Sort Out Techniques for Case Base Maintenance -- Entropy-Based vs. Similarity-Influenced: Attribute Selection Methods for Dialogs Tested on Different Electronic Commerce Domains -- Category-Based Filtering and User Stereotype Cases to Reduce the Latency Problem in Recommender Systems -- Defining Similarity Measures: Top-Down vs. Bottom-Up -- Learning to Adapt for Case-Based Design -- An Approach to Aggregating Ensembles of Lazy Learners That Supports Explanation -- An Experimental Study of Increasing Diversity for Case-Based Diagnosis -- Application Papers -- Collaborative Case-Based Recommender Systems -- Tuning Production Processes through a Case Based Reasoning Approach -- An Application of Case-Based Reasoning to the Adaptive Management of Wireless Networks -- A Case-Based Personal Travel Assistant for Elaborating User Requirements and Assessing Offers -- An Automated Hybrid CBR System for Forecasting -- Using CBR for Automation of Software Design Patterns -- A New Approach to Solution Adaptation and Its Application for Design Purposes -- InfoFrax: CBR in Fused Cast Refractory Manufacture -- Comparison-Based Recommendation -- Case-Based Reasoning for Estuarine Model Design -- Similarity Guided Learning of the Case Description and Improvement of the System Performance in an Image Classification System -- ITR: A Case-Based Travel Advisory System -- Supporting Electronic Design Reuse by Integrating Quality-Criteria into CBR-Based IP Selection -- Building a Case-Based Decision Support System for Land Development Control Using Land Use Function Pattern. |
| Record Nr. | UNISA-996466336803316 |
| Berlin, Germany ; ; New York, New York : , : Springer, , [2002] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Advances in case-based reasoning : 6th European conference, ECCBR 2002, Aberdeen, Scotland, UK, September 4-7, 2002, proceedings / / edited by Susan Craw, Alun Preece
| Advances in case-based reasoning : 6th European conference, ECCBR 2002, Aberdeen, Scotland, UK, September 4-7, 2002, proceedings / / edited by Susan Craw, Alun Preece |
| Edizione | [1st ed. 2002.] |
| Pubbl/distr/stampa | Berlin, Germany ; ; New York, New York : , : Springer, , [2002] |
| Descrizione fisica | 1 online resource (XII, 656 p.) |
| Disciplina | 006.33 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Expert systems (Computer science)
Case-based reasoning |
| ISBN | 3-540-46119-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Invited Papers -- Integrating Background Knowledge into Nearest-Neighbor Text Classification -- Applying Knowledge Management: Techniques for Building Organisational Memories -- Research Papers -- On the Complexity of Plan Adaptation by Derivational Analogy in a Universal Classical Planning Framework -- Inductive Learning for Case-Based Diagnosis with Multiple Faults -- Diverse Product Recommendations Using an Expressive Language for Case Retrieval -- Digital Image Similarity for Geo-spatial Knowledge Management -- Poetry Generation in COLIBRI -- Adaptation Using Iterated Estimations -- The Use of a Uniform Declarative Model in 3D Visualisation for Case-Based Reasoning -- Experiments on Case-Based Retrieval of Software Designs -- Exploiting Taxonomic and Causal Relations in Conversational Case Retrieval -- Bayesian Case Reconstruction -- Relations between Customer Requirements, Performance Measures, and General Case Properties for Case Base Maintenance -- Representing Temporal Knowledge for Case-Based Prediction -- Local Predictions for Case-Based Plan Recognition -- Automatically Selecting Strategies for Multi-Case-Base Reasoning -- Diversity-Conscious Retrieval -- Improving Case Representation and Case Base Maintenance in Recommender Agents -- Similarity Assessment for Generalizied Cases by Optimization Methods -- Case Acquisition in a Project Planning Environment -- Improving Case-Based Recommendation -- Efficient Similarity Determination and Case Construction Techniques for Case-Based Reasoning -- Constructive Adaptation -- A Fuzzy Case Retrieval Approach Based on SQL for Implementing Electronic Catalogs -- Integrating Hybrid Rule-Based with Case-Based Reasoning -- Search and Adaptation in a Fuzzy Object Oriented Case Base -- Deleting and Building Sort Out Techniques for Case Base Maintenance -- Entropy-Based vs. Similarity-Influenced: Attribute Selection Methods for Dialogs Tested on Different Electronic Commerce Domains -- Category-Based Filtering and User Stereotype Cases to Reduce the Latency Problem in Recommender Systems -- Defining Similarity Measures: Top-Down vs. Bottom-Up -- Learning to Adapt for Case-Based Design -- An Approach to Aggregating Ensembles of Lazy Learners That Supports Explanation -- An Experimental Study of Increasing Diversity for Case-Based Diagnosis -- Application Papers -- Collaborative Case-Based Recommender Systems -- Tuning Production Processes through a Case Based Reasoning Approach -- An Application of Case-Based Reasoning to the Adaptive Management of Wireless Networks -- A Case-Based Personal Travel Assistant for Elaborating User Requirements and Assessing Offers -- An Automated Hybrid CBR System for Forecasting -- Using CBR for Automation of Software Design Patterns -- A New Approach to Solution Adaptation and Its Application for Design Purposes -- InfoFrax: CBR in Fused Cast Refractory Manufacture -- Comparison-Based Recommendation -- Case-Based Reasoning for Estuarine Model Design -- Similarity Guided Learning of the Case Description and Improvement of the System Performance in an Image Classification System -- ITR: A Case-Based Travel Advisory System -- Supporting Electronic Design Reuse by Integrating Quality-Criteria into CBR-Based IP Selection -- Building a Case-Based Decision Support System for Land Development Control Using Land Use Function Pattern. |
| Record Nr. | UNINA-9910768452203321 |
| Berlin, Germany ; ; New York, New York : , : Springer, , [2002] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Case-based reasoning [[electronic resource] ] : processes, suitability and applications / / Antonia M. Leeland, editor
| Case-based reasoning [[electronic resource] ] : processes, suitability and applications / / Antonia M. Leeland, editor |
| Pubbl/distr/stampa | Hauppauge, N.Y., : Nova Science Publishers, c2011 |
| Descrizione fisica | 1 online resource (183 p.) |
| Disciplina | 153.4/3 |
| Altri autori (Persone) | LeelandAntonia M |
| Collana | Engineering tools, techniques and tables |
| Soggetto topico | Case-based reasoning |
| Soggetto genere / forma | Electronic books. |
| ISBN | 1-61728-814-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910461654003321 |
| Hauppauge, N.Y., : Nova Science Publishers, c2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Case-based reasoning [[electronic resource] ] : processes, suitability and applications / / Antonia M. Leeland, editor
| Case-based reasoning [[electronic resource] ] : processes, suitability and applications / / Antonia M. Leeland, editor |
| Pubbl/distr/stampa | Hauppauge, N.Y., : Nova Science Publishers, c2011 |
| Descrizione fisica | 1 online resource (183 p.) |
| Disciplina | 153.4/3 |
| Altri autori (Persone) | LeelandAntonia M |
| Collana | Engineering tools, techniques and tables |
| Soggetto topico | Case-based reasoning |
| ISBN | 1-61728-814-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910790280303321 |
| Hauppauge, N.Y., : Nova Science Publishers, c2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Case-based reasoning [[electronic resource] ] : processes, suitability and applications / / Antonia M. Leeland, editor
| Case-based reasoning [[electronic resource] ] : processes, suitability and applications / / Antonia M. Leeland, editor |
| Pubbl/distr/stampa | Hauppauge, N.Y., : Nova Science Publishers, c2011 |
| Descrizione fisica | 1 online resource (183 p.) |
| Disciplina | 153.4/3 |
| Altri autori (Persone) | LeelandAntonia M |
| Collana | Engineering tools, techniques and tables |
| Soggetto topico | Case-based reasoning |
| ISBN | 1-61728-814-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910826775603321 |
| Hauppauge, N.Y., : Nova Science Publishers, c2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Case-based reasoning research and development : 29th International Conference, ICCBR 2021, Salamanca, Spain, September 13-16, 2021 : proceedings / / Antonio A. Sánchez-Ruiz, Michael W. Floyd
| Case-based reasoning research and development : 29th International Conference, ICCBR 2021, Salamanca, Spain, September 13-16, 2021 : proceedings / / Antonio A. Sánchez-Ruiz, Michael W. Floyd |
| Autore | Sánchez-Ruiz Antonio A. |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer International Publishing, , [2021] |
| Descrizione fisica | 1 online resource (337 pages) |
| Disciplina | 006.33 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Expert systems (Computer science)
Case-based reasoning |
| ISBN | 3-030-86957-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- The Bites Eclectic: Critique-Based Conversational Recommendation for Diversity-Focused Meal Planning -- 1 Introduction -- 2 Background -- 2.1 Diversity -- 2.2 Critiquing -- 2.3 Recipe Recommendation and Diversity -- 3 DiversityBite: A Conversational Dynamic Critique-Based Recommender -- 3.1 Recipe Case Representation and Similarity -- 3.2 Diversity-Focused Conversational Critique -- 4 Evaluation for DiversityBite -- 4.1 Recipe Dataset -- 4.2 Evaluation Study -- 4.3 Evaluation Results -- 5 Discussion and Future Work -- References -- Evaluation of Similarity Measures for Flight Simulator Training Scenarios -- 1 Introduction -- 2 Related Work -- 3 Problem Definition: Pilot Competencies and Event Sets -- 4 Case Retrieval of Scenario Event Sets -- 5 Evaluation of Similarity Measures -- 5.1 Experimental Setup -- 5.2 Analysis -- 6 Results and Discussion -- 7 Conclusions -- References -- Instance-Based Counterfactual Explanations for Time Series Classification -- 1 Introduction -- 2 Related Work -- 3 Good Counterfactuals for Time Series: Key Properties -- 4 Native Guide: Counterfactual XAI for Time Series -- 5 Testing Native Guide: Two Comparative Experiments -- 5.1 Experiment 1: Probing Proximity and Sparsity -- 5.2 Experiment 2: Exploring Plausibility and Diversity -- 6 Conclusion and Future Directions -- References -- User Evaluation to Measure the Perception of Similarity Measures in Artworks -- 1 Introduction -- 2 Related Work About Similarity -- 3 Methodology for Learning Similarity Measures Reflecting Human Perceptions -- 3.1 Methodology for Learning Perception Aware Similarity Measures -- 3.2 Profile Definition -- 4 Experiment on the Perception of Similarity for Artworks -- 4.1 Definition of Local Similarity Measures -- 4.2 Data Gathering of Perceived Similarity -- 4.3 Experimental Results.
5 Conclusions and Future Work -- References -- Measuring Financial Time Series Similarity with a View to Identifying Profitable Stock Market Opportunities -- 1 Introduction -- 2 Related Work -- 3 From Prices to Cases -- 4 Similarity in Financial Time Series -- 4.1 The Problem with Correlation -- 4.2 An Adjusted Correlation Metric -- 4.3 A Novel Similarity Metric for Returns-Based Time-Series -- 4.4 Most and Least Similar Cases -- 5 Evaluation -- 5.1 Predicting Monthly Returns -- 5.2 Comparing Trading Strategies -- 6 Conclusion and Future Work -- References -- A Case-Based Reasoning Approach to Predicting and Explaining Running Related Injuries -- 1 Introduction -- 2 Related Work -- 3 CBR for Injury Prediction and Explanation -- 3.1 Representing Training Load -- 3.2 Representing Injury Cases -- 3.3 Balancing the Case Base -- 3.4 Task 1: Predicting Training Breaks -- 3.5 Task 2: Explaining Training Breaks -- 4 Evaluation -- 4.1 Setup -- 4.2 Evaluating Prediction Accuracy -- 4.3 Evaluating Injury Explanations -- 5 Conclusions -- References -- Bayesian Feature Construction for Case-Based Reasoning: Generating Good Checklists -- 1 Introduction -- 2 Related Work -- 3 Case and Problem Definition -- 4 BCBR Framework -- 4.1 Bayesian Inference -- 4.2 Case Base Creation and CBR Engine -- 4.3 Example: NBI Estimates, Case Retrieval and CBR Case -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Experiment 1: Answer Classification Performance (Baselines) -- 5.3 Experiment 2: Trustworthiness of Constructed Checklists -- 5.4 Experiment 3: Evaluation of Constructed Checklists -- 6 Conclusion -- References -- Revisiting Fast and Slow Thinking in Case-Based Reasoning -- 1 Introduction -- 2 Background -- 3 Feature Selection -- 3.1 Feature Selection for Parsimonious Search -- 3.2 Modelling Desirability of Feature Sets -- 3.3 Brute Force Approach -- 3.4 Greedy Approach. 3.5 Experimental Analysis -- 4 Constraint-Based Switching -- 4.1 Model 2D -- 4.2 Experimental Analysis -- 5 Complexity Measure for Dichotomous Models -- 5.1 Footprint Size -- 5.2 Footprint with Time -- 5.3 Experimental Analysis -- 6 Conclusion and Future Work -- References -- Harmonizing Case Retrieval and Adaptation with Alternating Optimization -- 1 Introduction -- 2 Background -- 3 Alternating Optimization of Retrieval and Adaptation -- 4 Testbed System Design -- 4.1 Loss Function -- 4.2 Testbed Retrieval and Adaptation -- 4.3 Testbed Training and Testing Procedures -- 5 Evaluation -- 5.1 Experimental Settings -- 5.2 Experimental Results -- 6 Guidelines for Applying AO to Train CBR Components -- 7 Future Work -- 8 Conclusion -- References -- Adaptation Knowledge Discovery Using Positive and Negative Cases -- 1 Introduction -- 2 Motivations and Preliminaries -- 2.1 Assumptions and Notations About CBR -- 2.2 Boolean Setting Illustrated with a Boolean Function Example -- 2.3 Itemset Extraction -- 3 Exploiting Case Variations for Adaptation Knowledge Discovery with Positive and Negative Cases -- 4 Experiments on Benchmarks -- 4.1 Experiment Setting and Evaluation Methodology -- 4.2 Congressional Voting Records -- 4.3 Tic Tac Toe Endgame -- 4.4 Cardiac Diagnosis -- 4.5 Car Evaluation -- 4.6 Results and Discussion -- 5 Conclusion -- References -- When Revision-Based Case Adaptation Meets Analogical Extrapolation -- 1 Introduction -- 2 Setting of the Problem and Running Example -- 2.1 A Quick Refresher About Propositional Logic -- 2.2 Notions and Notations Related to CBR -- 2.3 Specification of the Running Example -- 2.4 Analogical Proportions and CBR -- 2.5 Belief Revision and CBR -- 3 Bridging Extrapolation and Revision-Based Adaptation -- 3.1 Reformulating Adaptation by Extrapolation as a Single Case Adaptation. 3.2 A Revision Operator Based on Competence of Case Pairs -- 3.3 An Approach to Adaptation Based on Extrapolation and Revision -- 3.4 Synthesis -- 4 Related Work and Final Remarks -- References -- Inferring Case-Based Reasoners' Knowledge to Enhance Interactivity -- 1 Introduction -- 2 Problem Statement: Interaction with a CBR Agent -- 3 Modeling the User as a Case-Based Reasoner -- 4 Inference of the CBR Parameters -- 4.1 General Principle -- 4.2 Inference of the Parameters for a Deterministic CBR -- 4.3 Probability of Retrieval for kNN -- 4.4 Discussion on the Inference Process -- 5 Application: Teaching Word Inflection -- 5.1 Presentation of the Application -- 5.2 Implementation of a Case-Based Reasoning Learner -- 5.3 Empirical Evaluation -- 6 Conclusion -- References -- A Case-Based Approach for the Selection of Explanation Algorithms in Image Classification -- 1 Introduction -- 2 Background -- 2.1 LIME -- 2.2 Anchors -- 2.3 Integrated Gradients -- 2.4 XRAI -- 3 CBR Process Specification -- 3.1 Case Base Elicitation -- 3.2 Similarity Metrics -- 3.3 Reuse Strategies -- 4 Evaluation -- 5 Conclusions and Future Work -- References -- Towards Richer Realizations of Holographic CBR -- 1 Introduction -- 2 Holographic Case-Based Reasoning -- 3 Methodology -- 3.1 Key Ideas -- 3.2 Holographic CBR Realization Framework -- 4 Results and Interpretations -- 4.1 Comparison with Baseline -- 4.2 Tests for Efficiency -- 5 Conclusions and Future Directions -- References -- Handling Climate Change Using Counterfactuals: Using Counterfactuals in Data Augmentation to Predict Crop Growth in an Uncertain Climate Future -- 1 Introduction -- 1.1 The Problem: Grass Growth Prediction for Sustainable Dairy Farming -- 1.2 Related Work: Counterfactuals from XAI to Data Augmentation -- 1.3 Research Questions and Novelties. 2 Study 1: Predicting Climate Disruption with PBI-CBR -- 2.1 Defining a Class Boundary for Climate Outlier Cases -- 2.2 Experiment 1a: The Contribution of Climate Outliers to Predictions -- 2.3 Experiment 1b: Role of Training Outliers at Values of k -- 3 Study 2: Predicting Climate Disruption with Counterfactuals -- 3.1 A Case-Based Counterfactual Augmentation Algorithm (CFA) -- 3.2 Experiment 2: Using Synthetic Counterfactual Cases to Predict Growth -- 4 Conclusions: Novelties, Explications and Caveats -- References -- A Case-Based Approach to Data-to-Text Generation -- 1 Introduction -- 2 Related Works -- 3 Background -- 4 Methodology -- 4.1 Case-Base Creation -- 4.2 Retrieval and Feature Weighting -- 4.3 Generation -- 5 Experimental Setup -- 5.1 Dataset -- 5.2 Baseline and Benchmark -- 5.3 Evaluation Methods -- 6 Results and Discussion -- 6.1 Comparison with Benchmark and Baseline -- 6.2 Ablation Studies -- 6.3 Qualitative Analysis -- 7 Conclusion and Future Work -- References -- On Combining Knowledge-Engineered and Network-Extracted Features for Retrieval -- 1 Introduction -- 2 Convolutional Neural Networks for Classification -- 3 Related Work -- 4 Bridging Engineered and Network-Extracted Features -- 5 Evaluation -- 5.1 Test Domain and Testbed System -- 5.2 Preliminary Experiments to Set Network Parameters -- 5.3 How Retrieval Accuracy Changes with KE Feature Degradation -- 5.4 How Using KE and NL Features in Concert Affects Accuracy -- 5.5 How Learned Weights Further Influence Retrieval Accuracy -- 6 Ramifications for Explainability -- 7 Conclusions -- References -- Task and Situation Structures for Case-Based Planning -- 1 Introduction -- 2 Task Structure and Planning -- 2.1 Task Structure -- 2.2 Execution of Tasks -- 2.3 Implementation of Our Task Structure -- 2.4 Discussions on Task Structure -- 3 Situation Structure -- 4 Situation Handling. 5 Illustrative Examples. |
| Record Nr. | UNINA-9910502645803321 |
Sánchez-Ruiz Antonio A.
|
||
| Cham, Switzerland : , : Springer International Publishing, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Case-based reasoning research and development : 29th International Conference, ICCBR 2021, Salamanca, Spain, September 13-16, 2021 : proceedings / / Antonio A. Sánchez-Ruiz, Michael W. Floyd
| Case-based reasoning research and development : 29th International Conference, ICCBR 2021, Salamanca, Spain, September 13-16, 2021 : proceedings / / Antonio A. Sánchez-Ruiz, Michael W. Floyd |
| Autore | Sánchez-Ruiz Antonio A. |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer International Publishing, , [2021] |
| Descrizione fisica | 1 online resource (337 pages) |
| Disciplina | 006.33 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Expert systems (Computer science)
Case-based reasoning |
| ISBN | 3-030-86957-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- The Bites Eclectic: Critique-Based Conversational Recommendation for Diversity-Focused Meal Planning -- 1 Introduction -- 2 Background -- 2.1 Diversity -- 2.2 Critiquing -- 2.3 Recipe Recommendation and Diversity -- 3 DiversityBite: A Conversational Dynamic Critique-Based Recommender -- 3.1 Recipe Case Representation and Similarity -- 3.2 Diversity-Focused Conversational Critique -- 4 Evaluation for DiversityBite -- 4.1 Recipe Dataset -- 4.2 Evaluation Study -- 4.3 Evaluation Results -- 5 Discussion and Future Work -- References -- Evaluation of Similarity Measures for Flight Simulator Training Scenarios -- 1 Introduction -- 2 Related Work -- 3 Problem Definition: Pilot Competencies and Event Sets -- 4 Case Retrieval of Scenario Event Sets -- 5 Evaluation of Similarity Measures -- 5.1 Experimental Setup -- 5.2 Analysis -- 6 Results and Discussion -- 7 Conclusions -- References -- Instance-Based Counterfactual Explanations for Time Series Classification -- 1 Introduction -- 2 Related Work -- 3 Good Counterfactuals for Time Series: Key Properties -- 4 Native Guide: Counterfactual XAI for Time Series -- 5 Testing Native Guide: Two Comparative Experiments -- 5.1 Experiment 1: Probing Proximity and Sparsity -- 5.2 Experiment 2: Exploring Plausibility and Diversity -- 6 Conclusion and Future Directions -- References -- User Evaluation to Measure the Perception of Similarity Measures in Artworks -- 1 Introduction -- 2 Related Work About Similarity -- 3 Methodology for Learning Similarity Measures Reflecting Human Perceptions -- 3.1 Methodology for Learning Perception Aware Similarity Measures -- 3.2 Profile Definition -- 4 Experiment on the Perception of Similarity for Artworks -- 4.1 Definition of Local Similarity Measures -- 4.2 Data Gathering of Perceived Similarity -- 4.3 Experimental Results.
5 Conclusions and Future Work -- References -- Measuring Financial Time Series Similarity with a View to Identifying Profitable Stock Market Opportunities -- 1 Introduction -- 2 Related Work -- 3 From Prices to Cases -- 4 Similarity in Financial Time Series -- 4.1 The Problem with Correlation -- 4.2 An Adjusted Correlation Metric -- 4.3 A Novel Similarity Metric for Returns-Based Time-Series -- 4.4 Most and Least Similar Cases -- 5 Evaluation -- 5.1 Predicting Monthly Returns -- 5.2 Comparing Trading Strategies -- 6 Conclusion and Future Work -- References -- A Case-Based Reasoning Approach to Predicting and Explaining Running Related Injuries -- 1 Introduction -- 2 Related Work -- 3 CBR for Injury Prediction and Explanation -- 3.1 Representing Training Load -- 3.2 Representing Injury Cases -- 3.3 Balancing the Case Base -- 3.4 Task 1: Predicting Training Breaks -- 3.5 Task 2: Explaining Training Breaks -- 4 Evaluation -- 4.1 Setup -- 4.2 Evaluating Prediction Accuracy -- 4.3 Evaluating Injury Explanations -- 5 Conclusions -- References -- Bayesian Feature Construction for Case-Based Reasoning: Generating Good Checklists -- 1 Introduction -- 2 Related Work -- 3 Case and Problem Definition -- 4 BCBR Framework -- 4.1 Bayesian Inference -- 4.2 Case Base Creation and CBR Engine -- 4.3 Example: NBI Estimates, Case Retrieval and CBR Case -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Experiment 1: Answer Classification Performance (Baselines) -- 5.3 Experiment 2: Trustworthiness of Constructed Checklists -- 5.4 Experiment 3: Evaluation of Constructed Checklists -- 6 Conclusion -- References -- Revisiting Fast and Slow Thinking in Case-Based Reasoning -- 1 Introduction -- 2 Background -- 3 Feature Selection -- 3.1 Feature Selection for Parsimonious Search -- 3.2 Modelling Desirability of Feature Sets -- 3.3 Brute Force Approach -- 3.4 Greedy Approach. 3.5 Experimental Analysis -- 4 Constraint-Based Switching -- 4.1 Model 2D -- 4.2 Experimental Analysis -- 5 Complexity Measure for Dichotomous Models -- 5.1 Footprint Size -- 5.2 Footprint with Time -- 5.3 Experimental Analysis -- 6 Conclusion and Future Work -- References -- Harmonizing Case Retrieval and Adaptation with Alternating Optimization -- 1 Introduction -- 2 Background -- 3 Alternating Optimization of Retrieval and Adaptation -- 4 Testbed System Design -- 4.1 Loss Function -- 4.2 Testbed Retrieval and Adaptation -- 4.3 Testbed Training and Testing Procedures -- 5 Evaluation -- 5.1 Experimental Settings -- 5.2 Experimental Results -- 6 Guidelines for Applying AO to Train CBR Components -- 7 Future Work -- 8 Conclusion -- References -- Adaptation Knowledge Discovery Using Positive and Negative Cases -- 1 Introduction -- 2 Motivations and Preliminaries -- 2.1 Assumptions and Notations About CBR -- 2.2 Boolean Setting Illustrated with a Boolean Function Example -- 2.3 Itemset Extraction -- 3 Exploiting Case Variations for Adaptation Knowledge Discovery with Positive and Negative Cases -- 4 Experiments on Benchmarks -- 4.1 Experiment Setting and Evaluation Methodology -- 4.2 Congressional Voting Records -- 4.3 Tic Tac Toe Endgame -- 4.4 Cardiac Diagnosis -- 4.5 Car Evaluation -- 4.6 Results and Discussion -- 5 Conclusion -- References -- When Revision-Based Case Adaptation Meets Analogical Extrapolation -- 1 Introduction -- 2 Setting of the Problem and Running Example -- 2.1 A Quick Refresher About Propositional Logic -- 2.2 Notions and Notations Related to CBR -- 2.3 Specification of the Running Example -- 2.4 Analogical Proportions and CBR -- 2.5 Belief Revision and CBR -- 3 Bridging Extrapolation and Revision-Based Adaptation -- 3.1 Reformulating Adaptation by Extrapolation as a Single Case Adaptation. 3.2 A Revision Operator Based on Competence of Case Pairs -- 3.3 An Approach to Adaptation Based on Extrapolation and Revision -- 3.4 Synthesis -- 4 Related Work and Final Remarks -- References -- Inferring Case-Based Reasoners' Knowledge to Enhance Interactivity -- 1 Introduction -- 2 Problem Statement: Interaction with a CBR Agent -- 3 Modeling the User as a Case-Based Reasoner -- 4 Inference of the CBR Parameters -- 4.1 General Principle -- 4.2 Inference of the Parameters for a Deterministic CBR -- 4.3 Probability of Retrieval for kNN -- 4.4 Discussion on the Inference Process -- 5 Application: Teaching Word Inflection -- 5.1 Presentation of the Application -- 5.2 Implementation of a Case-Based Reasoning Learner -- 5.3 Empirical Evaluation -- 6 Conclusion -- References -- A Case-Based Approach for the Selection of Explanation Algorithms in Image Classification -- 1 Introduction -- 2 Background -- 2.1 LIME -- 2.2 Anchors -- 2.3 Integrated Gradients -- 2.4 XRAI -- 3 CBR Process Specification -- 3.1 Case Base Elicitation -- 3.2 Similarity Metrics -- 3.3 Reuse Strategies -- 4 Evaluation -- 5 Conclusions and Future Work -- References -- Towards Richer Realizations of Holographic CBR -- 1 Introduction -- 2 Holographic Case-Based Reasoning -- 3 Methodology -- 3.1 Key Ideas -- 3.2 Holographic CBR Realization Framework -- 4 Results and Interpretations -- 4.1 Comparison with Baseline -- 4.2 Tests for Efficiency -- 5 Conclusions and Future Directions -- References -- Handling Climate Change Using Counterfactuals: Using Counterfactuals in Data Augmentation to Predict Crop Growth in an Uncertain Climate Future -- 1 Introduction -- 1.1 The Problem: Grass Growth Prediction for Sustainable Dairy Farming -- 1.2 Related Work: Counterfactuals from XAI to Data Augmentation -- 1.3 Research Questions and Novelties. 2 Study 1: Predicting Climate Disruption with PBI-CBR -- 2.1 Defining a Class Boundary for Climate Outlier Cases -- 2.2 Experiment 1a: The Contribution of Climate Outliers to Predictions -- 2.3 Experiment 1b: Role of Training Outliers at Values of k -- 3 Study 2: Predicting Climate Disruption with Counterfactuals -- 3.1 A Case-Based Counterfactual Augmentation Algorithm (CFA) -- 3.2 Experiment 2: Using Synthetic Counterfactual Cases to Predict Growth -- 4 Conclusions: Novelties, Explications and Caveats -- References -- A Case-Based Approach to Data-to-Text Generation -- 1 Introduction -- 2 Related Works -- 3 Background -- 4 Methodology -- 4.1 Case-Base Creation -- 4.2 Retrieval and Feature Weighting -- 4.3 Generation -- 5 Experimental Setup -- 5.1 Dataset -- 5.2 Baseline and Benchmark -- 5.3 Evaluation Methods -- 6 Results and Discussion -- 6.1 Comparison with Benchmark and Baseline -- 6.2 Ablation Studies -- 6.3 Qualitative Analysis -- 7 Conclusion and Future Work -- References -- On Combining Knowledge-Engineered and Network-Extracted Features for Retrieval -- 1 Introduction -- 2 Convolutional Neural Networks for Classification -- 3 Related Work -- 4 Bridging Engineered and Network-Extracted Features -- 5 Evaluation -- 5.1 Test Domain and Testbed System -- 5.2 Preliminary Experiments to Set Network Parameters -- 5.3 How Retrieval Accuracy Changes with KE Feature Degradation -- 5.4 How Using KE and NL Features in Concert Affects Accuracy -- 5.5 How Learned Weights Further Influence Retrieval Accuracy -- 6 Ramifications for Explainability -- 7 Conclusions -- References -- Task and Situation Structures for Case-Based Planning -- 1 Introduction -- 2 Task Structure and Planning -- 2.1 Task Structure -- 2.2 Execution of Tasks -- 2.3 Implementation of Our Task Structure -- 2.4 Discussions on Task Structure -- 3 Situation Structure -- 4 Situation Handling. 5 Illustrative Examples. |
| Record Nr. | UNISA-996464529403316 |
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Sánchez-Ruiz Antonio A.
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| Cham, Switzerland : , : Springer International Publishing, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Case-based reasoning research and development : 30th International Conference, ICCBR 2022, Nancy, France, September 12-15, 2022, proceedings / / Mark T. Keane, Nirmalie Wiratunga (editors)
| Case-based reasoning research and development : 30th International Conference, ICCBR 2022, Nancy, France, September 12-15, 2022, proceedings / / Mark T. Keane, Nirmalie Wiratunga (editors) |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (420 pages) |
| Disciplina | 153.43 |
| Collana | Lecture notes in computer science. Lecture notes in artificial intelligence |
| Soggetto topico |
Case-based reasoning
Expert systems (Computer science) Deep learning (Machine learning) |
| ISBN | 3-031-14923-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Invited Talks -- Seeing Through Black Boxes with Human Vision: Deep Learning and Explainable AI in Medical Image Applications -- Case-Based Reasoning for Clinical Decisions That Are Computer-Aided, Not Automated -- Towards More Cognitively Appealing Paradigms in Case-Based Reasoning -- Contents -- Explainability in CBR -- Using Case-Based Reasoning for Capturing Expert Knowledge on Explanation Methods -- 1 Introduction -- 2 Background -- 3 Case-Based Elicitation -- 3.1 Case Structure -- 3.2 Case Base Acquisition -- 4 CBR Process -- 5 Evaluation and Discussion -- 6 Conclusions -- References -- A Few Good Counterfactuals: Generating Interpretable, Plausible and Diverse Counterfactual Explanations -- 1 Introduction -- 2 Related Work -- 2.1 What Are Good Counterfactual Explanations? -- 2.2 Perturbation-Based Approaches -- 2.3 Instance-Based Approaches -- 2.4 Instance-Based Shortcomings -- 3 Good Counterfactuals in Multi-class Domains -- 3.1 Reusing the kNN Explanation Cases -- 3.2 Validating Candidate Counterfactuals -- 3.3 Discussion -- 4 Evaluation -- 4.1 Methodology -- 4.2 Results -- 5 Conclusions -- References -- How Close Is Too Close? The Role of Feature Attributions in Discovering Counterfactual Explanations -- 1 Introduction -- 2 Related Work -- 3 DisCERN -- 3.1 Nearest-Unlike Neighbour -- 3.2 Feature Ordering by Feature Attribution -- 3.3 Substitution-Based Adaptation -- 3.4 Integrated Gradients for DisCERN -- 3.5 Bringing the NUN Closer -- 4 Evaluation -- 4.1 Datasets -- 4.2 Experiment Setup -- 4.3 Performance Measures for Counterfactual Explanations -- 5 Results -- 5.1 A Comparison of Feature Attribution Techniques -- 5.2 A Comparison of Counterfactual Discovery Algorithms -- 5.3 Impact of Bringing NUN Closer -- 6 Conclusions -- References -- Algorithmic Bias and Fairness in Case-Based Reasoning.
1 Introduction -- 2 Related Research -- 2.1 Bias in ML -- 2.2 Bias in CBR -- 2.3 Metric Learning -- 3 FairRet: Eliminating Bias with Metric Learning -- 3.1 Bias and The Similarity Knowledge Container -- 3.2 A Metric Learning Approach -- 3.3 Multi-objective Particle Swarm Optimization -- 4 Results -- 4.1 Dealing with Underestimation Bias -- 4.2 Outcome Distortion -- 4.3 Retrieval Overlap -- 5 Conclusions -- References -- "Better" Counterfactuals, Ones People Can Understand: Psychologically-Plausible Case-Based Counterfactuals Using Categorical Features for Explainable AI (XAI) -- 1 Introduction -- 2 Background: Computation and Psychology of Counterfactuals -- 2.1 User Studies of Counterfactual XAI: Mixed Results -- 3 Study 1: Plotting Counterfactuals that have Categoricals -- 3.1 Results and Discussion -- 4 Transforming Case-Based Counterfactuals, Categorically -- 4.1 Case-Based Counterfactual Methods: CB1-CF and CB2-CF -- 4.2 Counterfactuals with Categorical Transforms #1: Global Binning -- 4.3 Counterfactuals with Categorical Transforms #2: Local Direction -- 5 Study 2: Evaluating CAT-CF Methods -- 5.1 Method: Data and Procedure -- 5.2 Results and Discussion: Counterfactual Distance -- 6 Conclusions -- References -- Representation and Similarity -- Extracting Case Indices from Convolutional Neural Networks: A Comparative Study -- 1 Introduction -- 2 Potential Feature Extraction Points in cnns -- 3 Related Work -- 4 Three Structure-Based Feature Extraction Methods -- 4.1 Post-convolution Feature Extraction -- 4.2 Post-dense Feature Extraction -- 4.3 Multi-net Feature Extraction -- 5 Evaluation -- 5.1 Hypotheses -- 5.2 Test Domain and Test Set Selection -- 5.3 Testbed System -- 5.4 Accuracy Testing and Informal Upper Bound -- 6 Results and Discussion -- 6.1 Comparative Performance -- 6.2 Discussion -- 7 Ramifications for Interpretability. 8 Conclusions and Future Work -- References -- Exploring the Effect of Recipe Representation on Critique-Based Conversational Recommendation -- 1 Introduction -- 2 Background -- 2.1 Diversity in Recommender Systems -- 2.2 Critique-Based Conversational Recommender Systems -- 2.3 Diversity in Recipe Recommenders -- 3 DiversityBite Framework: Recommend, Review, Revise -- 3.1 Adaptive Diversity Goal Approach -- 4 Evaluation -- 4.1 Case Base -- 4.2 Implementation: DGF, AGD, and Diversity Scoring -- 4.3 Simulation Study: Incorporating Diversity in Critique -- 4.4 User Study: Comparing Different Recipe Representations -- 5 Conclusion -- References -- Explaining CBR Systems Through Retrieval and Similarity Measure Visualizations: A Case Study -- 1 Introduction -- 2 Related Work -- 3 SupportPrim CBR System -- 3.1 Data -- 3.2 Case Representation and Similarity Modeling -- 3.3 Case Base and Similarity Population -- 4 Explanatory Case Base Visualizations -- 4.1 Accessing the CBR System's Model -- 4.2 Visualization of Retrievals -- 4.3 Visualization of the Similarity Scores for Individual Case Comparisons -- 5 Experiments -- 6 Discussion -- 7 Conclusion -- References -- Adapting Semantic Similarity Methods for Case-Based Reasoning in the Cloud -- 1 Introduction -- 2 Related Work -- 2.1 Clood CBR -- 2.2 Ontologies in CBR -- 2.3 Retrieval with Word Embedding -- 2.4 Serverless Function Benefits and Limitations -- 3 Semantic Similarity Metrics in a Microservices Architecture -- 3.1 Clood Similarity Functions Overview -- 3.2 Similarity Table -- 3.3 Word Embedding Based Similarity -- 3.4 Ontology-Based Similarity Measure -- 4 Implementation of Semantic Similarity Measures on Clood Framework -- 4.1 Word Embedding Similarity on Clood -- 4.2 Ontology-Based Similarity on Clood -- 5 Evaluation of Resource Impact -- 5.1 Experiment Setup -- 5.2 Result and Discussion. 6 Conclusion -- References -- Adaptation and Analogical Reasoning -- Case Adaptation with Neural Networks: Capabilities and Limitations -- 1 Introduction -- 2 Background -- 3 NN-CDH for both Classification and Regression -- 3.1 General Model of Case Adaptation -- 3.2 1-Hot/1-Cold Nominal Difference -- 3.3 Neural Network Structure of NN-CDH -- 3.4 Training and Adaptation Procedure -- 4 Evaluation -- 4.1 Systems Being Compared -- 4.2 Assembling Case Pairs for Training -- 4.3 Data Sets -- 4.4 Artificial Data Sets -- 5 Conclusion -- References -- A Deep Learning Approach to Solving Morphological Analogies -- 1 Introduction -- 2 The Problem of Morphological Analogy -- 3 Proposed Approach -- 3.1 Classification, Retrieval and Embedding Models -- 3.2 Training and Evaluation -- 4 Experiments -- 4.1 Data -- 4.2 Refining the Training Procedure -- 4.3 Performance Comparison with State of the Art Methods -- 4.4 Distance of the Expected Result -- 4.5 Case Analysis: Navajo and Georgian -- 5 Conclusion and Perspectives -- References -- Theoretical and Experimental Study of a Complexity Measure for Analogical Transfer -- 1 Introduction -- 2 Reminder on Complexity-Based Analogy -- 2.1 Notations -- 2.2 Ordinal Analogical Principle: Complexity Definition -- 2.3 Ordinal Analogical Inference Algorithm -- 3 Theoretical Property of the Complexity Measure: Upper Bound -- 3.1 General Case -- 3.2 Binary Classification Case -- 4 Algorithmic Optimisation -- 4.1 Principle -- 4.2 Proposed Optimized Algorithm -- 5 Experimental Study -- 5.1 Computational Cost -- 5.2 Correlation Between Case Base Complexity and Performance -- 5.3 Correlation Between Complexity and Task Difficulty -- 6 Conclusion and Future Works -- References -- Graphs and Optimisation -- Case-Based Learning and Reasoning Using Layered Boundary Multigraphs -- 1 Introduction -- 2 Background and Related Work. 3 Boundary Graphs and Labeled Boundary Multigraphs -- 3.1 Boundary Graphs -- 3.2 Labeled Boundary Multigraphs -- 3.3 Discussion -- 4 Empirical Evaluation -- 4.1 Experimental Set-Up -- 4.2 Classical Benchmark Data Sets -- 4.3 Scaling Analysis -- 5 Conclusion -- References -- Particle Swarm Optimization in Small Case Bases for Software Effort Estimation -- 1 Introduction -- 2 Related Work -- 3 Software Effort Estimation of User Stories -- 4 CBR Approach -- 4.1 Case Representation -- 4.2 Similarity -- 4.3 Adaptation -- 4.4 Weight Optimization with PSO -- 5 Experiments -- 5.1 Experimental Data -- 5.2 Experiment 1 -- 5.3 Experiment 2 -- 5.4 Discussion of Results -- 6 Conclusion -- References -- MicroCBR: Case-Based Reasoning on Spatio-temporal Fault Knowledge Graph for Microservices Troubleshooting -- 1 Introduction -- 2 Related Work -- 3 Background and Motivation -- 3.1 Background with Basic Concepts -- 3.2 Motivation -- 4 Troubleshooting Framework -- 4.1 Framework Overview -- 4.2 Spatio-Temporal Fault Knowledge Graph -- 4.3 Fingerprinting the Fault -- 4.4 Case-Based Reasoning -- 5 Evaluation -- 5.1 Evaluation Setup -- 5.2 Q1. Comparative Experiments -- 5.3 Q2. Ablation Experiment -- 5.4 Q3. Efficiency Experiments -- 5.5 Q4. Case Studies and Learned Lessons -- 6 Conclusion -- References -- .26em plus .1em minus .1emGPU-Based Graph Matching for Accelerating Similarity Assessment in Process-Oriented Case-Based Reasoning -- 1 Introduction -- 2 Foundations and Related Work -- 2.1 Semantic Workflow Graph Representation -- 2.2 State-Space Search by Using A* -- 2.3 Related Work -- 3 AMonG: A*-Based Graph Matching on Graphic Processing Units -- 3.1 Overview and Components -- 3.2 Parallel Graph Matching -- 4 Experimental Evaluation -- 4.1 Experimental Setup -- 4.2 Experimental Results -- 4.3 Discussion and Further Considerations -- 5 Conclusion and Future Work. References. |
| Record Nr. | UNINA-9910586636203321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Case-based reasoning research and development : 30th International Conference, ICCBR 2022, Nancy, France, September 12-15, 2022, proceedings / / Mark T. Keane, Nirmalie Wiratunga (editors)
| Case-based reasoning research and development : 30th International Conference, ICCBR 2022, Nancy, France, September 12-15, 2022, proceedings / / Mark T. Keane, Nirmalie Wiratunga (editors) |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (420 pages) |
| Disciplina | 153.43 |
| Collana | Lecture notes in computer science. Lecture notes in artificial intelligence |
| Soggetto topico |
Case-based reasoning
Expert systems (Computer science) Deep learning (Machine learning) |
| ISBN | 3-031-14923-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Invited Talks -- Seeing Through Black Boxes with Human Vision: Deep Learning and Explainable AI in Medical Image Applications -- Case-Based Reasoning for Clinical Decisions That Are Computer-Aided, Not Automated -- Towards More Cognitively Appealing Paradigms in Case-Based Reasoning -- Contents -- Explainability in CBR -- Using Case-Based Reasoning for Capturing Expert Knowledge on Explanation Methods -- 1 Introduction -- 2 Background -- 3 Case-Based Elicitation -- 3.1 Case Structure -- 3.2 Case Base Acquisition -- 4 CBR Process -- 5 Evaluation and Discussion -- 6 Conclusions -- References -- A Few Good Counterfactuals: Generating Interpretable, Plausible and Diverse Counterfactual Explanations -- 1 Introduction -- 2 Related Work -- 2.1 What Are Good Counterfactual Explanations? -- 2.2 Perturbation-Based Approaches -- 2.3 Instance-Based Approaches -- 2.4 Instance-Based Shortcomings -- 3 Good Counterfactuals in Multi-class Domains -- 3.1 Reusing the kNN Explanation Cases -- 3.2 Validating Candidate Counterfactuals -- 3.3 Discussion -- 4 Evaluation -- 4.1 Methodology -- 4.2 Results -- 5 Conclusions -- References -- How Close Is Too Close? The Role of Feature Attributions in Discovering Counterfactual Explanations -- 1 Introduction -- 2 Related Work -- 3 DisCERN -- 3.1 Nearest-Unlike Neighbour -- 3.2 Feature Ordering by Feature Attribution -- 3.3 Substitution-Based Adaptation -- 3.4 Integrated Gradients for DisCERN -- 3.5 Bringing the NUN Closer -- 4 Evaluation -- 4.1 Datasets -- 4.2 Experiment Setup -- 4.3 Performance Measures for Counterfactual Explanations -- 5 Results -- 5.1 A Comparison of Feature Attribution Techniques -- 5.2 A Comparison of Counterfactual Discovery Algorithms -- 5.3 Impact of Bringing NUN Closer -- 6 Conclusions -- References -- Algorithmic Bias and Fairness in Case-Based Reasoning.
1 Introduction -- 2 Related Research -- 2.1 Bias in ML -- 2.2 Bias in CBR -- 2.3 Metric Learning -- 3 FairRet: Eliminating Bias with Metric Learning -- 3.1 Bias and The Similarity Knowledge Container -- 3.2 A Metric Learning Approach -- 3.3 Multi-objective Particle Swarm Optimization -- 4 Results -- 4.1 Dealing with Underestimation Bias -- 4.2 Outcome Distortion -- 4.3 Retrieval Overlap -- 5 Conclusions -- References -- "Better" Counterfactuals, Ones People Can Understand: Psychologically-Plausible Case-Based Counterfactuals Using Categorical Features for Explainable AI (XAI) -- 1 Introduction -- 2 Background: Computation and Psychology of Counterfactuals -- 2.1 User Studies of Counterfactual XAI: Mixed Results -- 3 Study 1: Plotting Counterfactuals that have Categoricals -- 3.1 Results and Discussion -- 4 Transforming Case-Based Counterfactuals, Categorically -- 4.1 Case-Based Counterfactual Methods: CB1-CF and CB2-CF -- 4.2 Counterfactuals with Categorical Transforms #1: Global Binning -- 4.3 Counterfactuals with Categorical Transforms #2: Local Direction -- 5 Study 2: Evaluating CAT-CF Methods -- 5.1 Method: Data and Procedure -- 5.2 Results and Discussion: Counterfactual Distance -- 6 Conclusions -- References -- Representation and Similarity -- Extracting Case Indices from Convolutional Neural Networks: A Comparative Study -- 1 Introduction -- 2 Potential Feature Extraction Points in cnns -- 3 Related Work -- 4 Three Structure-Based Feature Extraction Methods -- 4.1 Post-convolution Feature Extraction -- 4.2 Post-dense Feature Extraction -- 4.3 Multi-net Feature Extraction -- 5 Evaluation -- 5.1 Hypotheses -- 5.2 Test Domain and Test Set Selection -- 5.3 Testbed System -- 5.4 Accuracy Testing and Informal Upper Bound -- 6 Results and Discussion -- 6.1 Comparative Performance -- 6.2 Discussion -- 7 Ramifications for Interpretability. 8 Conclusions and Future Work -- References -- Exploring the Effect of Recipe Representation on Critique-Based Conversational Recommendation -- 1 Introduction -- 2 Background -- 2.1 Diversity in Recommender Systems -- 2.2 Critique-Based Conversational Recommender Systems -- 2.3 Diversity in Recipe Recommenders -- 3 DiversityBite Framework: Recommend, Review, Revise -- 3.1 Adaptive Diversity Goal Approach -- 4 Evaluation -- 4.1 Case Base -- 4.2 Implementation: DGF, AGD, and Diversity Scoring -- 4.3 Simulation Study: Incorporating Diversity in Critique -- 4.4 User Study: Comparing Different Recipe Representations -- 5 Conclusion -- References -- Explaining CBR Systems Through Retrieval and Similarity Measure Visualizations: A Case Study -- 1 Introduction -- 2 Related Work -- 3 SupportPrim CBR System -- 3.1 Data -- 3.2 Case Representation and Similarity Modeling -- 3.3 Case Base and Similarity Population -- 4 Explanatory Case Base Visualizations -- 4.1 Accessing the CBR System's Model -- 4.2 Visualization of Retrievals -- 4.3 Visualization of the Similarity Scores for Individual Case Comparisons -- 5 Experiments -- 6 Discussion -- 7 Conclusion -- References -- Adapting Semantic Similarity Methods for Case-Based Reasoning in the Cloud -- 1 Introduction -- 2 Related Work -- 2.1 Clood CBR -- 2.2 Ontologies in CBR -- 2.3 Retrieval with Word Embedding -- 2.4 Serverless Function Benefits and Limitations -- 3 Semantic Similarity Metrics in a Microservices Architecture -- 3.1 Clood Similarity Functions Overview -- 3.2 Similarity Table -- 3.3 Word Embedding Based Similarity -- 3.4 Ontology-Based Similarity Measure -- 4 Implementation of Semantic Similarity Measures on Clood Framework -- 4.1 Word Embedding Similarity on Clood -- 4.2 Ontology-Based Similarity on Clood -- 5 Evaluation of Resource Impact -- 5.1 Experiment Setup -- 5.2 Result and Discussion. 6 Conclusion -- References -- Adaptation and Analogical Reasoning -- Case Adaptation with Neural Networks: Capabilities and Limitations -- 1 Introduction -- 2 Background -- 3 NN-CDH for both Classification and Regression -- 3.1 General Model of Case Adaptation -- 3.2 1-Hot/1-Cold Nominal Difference -- 3.3 Neural Network Structure of NN-CDH -- 3.4 Training and Adaptation Procedure -- 4 Evaluation -- 4.1 Systems Being Compared -- 4.2 Assembling Case Pairs for Training -- 4.3 Data Sets -- 4.4 Artificial Data Sets -- 5 Conclusion -- References -- A Deep Learning Approach to Solving Morphological Analogies -- 1 Introduction -- 2 The Problem of Morphological Analogy -- 3 Proposed Approach -- 3.1 Classification, Retrieval and Embedding Models -- 3.2 Training and Evaluation -- 4 Experiments -- 4.1 Data -- 4.2 Refining the Training Procedure -- 4.3 Performance Comparison with State of the Art Methods -- 4.4 Distance of the Expected Result -- 4.5 Case Analysis: Navajo and Georgian -- 5 Conclusion and Perspectives -- References -- Theoretical and Experimental Study of a Complexity Measure for Analogical Transfer -- 1 Introduction -- 2 Reminder on Complexity-Based Analogy -- 2.1 Notations -- 2.2 Ordinal Analogical Principle: Complexity Definition -- 2.3 Ordinal Analogical Inference Algorithm -- 3 Theoretical Property of the Complexity Measure: Upper Bound -- 3.1 General Case -- 3.2 Binary Classification Case -- 4 Algorithmic Optimisation -- 4.1 Principle -- 4.2 Proposed Optimized Algorithm -- 5 Experimental Study -- 5.1 Computational Cost -- 5.2 Correlation Between Case Base Complexity and Performance -- 5.3 Correlation Between Complexity and Task Difficulty -- 6 Conclusion and Future Works -- References -- Graphs and Optimisation -- Case-Based Learning and Reasoning Using Layered Boundary Multigraphs -- 1 Introduction -- 2 Background and Related Work. 3 Boundary Graphs and Labeled Boundary Multigraphs -- 3.1 Boundary Graphs -- 3.2 Labeled Boundary Multigraphs -- 3.3 Discussion -- 4 Empirical Evaluation -- 4.1 Experimental Set-Up -- 4.2 Classical Benchmark Data Sets -- 4.3 Scaling Analysis -- 5 Conclusion -- References -- Particle Swarm Optimization in Small Case Bases for Software Effort Estimation -- 1 Introduction -- 2 Related Work -- 3 Software Effort Estimation of User Stories -- 4 CBR Approach -- 4.1 Case Representation -- 4.2 Similarity -- 4.3 Adaptation -- 4.4 Weight Optimization with PSO -- 5 Experiments -- 5.1 Experimental Data -- 5.2 Experiment 1 -- 5.3 Experiment 2 -- 5.4 Discussion of Results -- 6 Conclusion -- References -- MicroCBR: Case-Based Reasoning on Spatio-temporal Fault Knowledge Graph for Microservices Troubleshooting -- 1 Introduction -- 2 Related Work -- 3 Background and Motivation -- 3.1 Background with Basic Concepts -- 3.2 Motivation -- 4 Troubleshooting Framework -- 4.1 Framework Overview -- 4.2 Spatio-Temporal Fault Knowledge Graph -- 4.3 Fingerprinting the Fault -- 4.4 Case-Based Reasoning -- 5 Evaluation -- 5.1 Evaluation Setup -- 5.2 Q1. Comparative Experiments -- 5.3 Q2. Ablation Experiment -- 5.4 Q3. Efficiency Experiments -- 5.5 Q4. Case Studies and Learned Lessons -- 6 Conclusion -- References -- .26em plus .1em minus .1emGPU-Based Graph Matching for Accelerating Similarity Assessment in Process-Oriented Case-Based Reasoning -- 1 Introduction -- 2 Foundations and Related Work -- 2.1 Semantic Workflow Graph Representation -- 2.2 State-Space Search by Using A* -- 2.3 Related Work -- 3 AMonG: A*-Based Graph Matching on Graphic Processing Units -- 3.1 Overview and Components -- 3.2 Parallel Graph Matching -- 4 Experimental Evaluation -- 4.1 Experimental Setup -- 4.2 Experimental Results -- 4.3 Discussion and Further Considerations -- 5 Conclusion and Future Work. References. |
| Record Nr. | UNISA-996485668603316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
