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Advances and Impacts of the Theory of Inventive Problem Solving [[electronic resource] ] : The TRIZ Methodology, Tools and Case Studies / / edited by Sebastian Koziołek, Leonid Chechurin, Mikael Collan
| Advances and Impacts of the Theory of Inventive Problem Solving [[electronic resource] ] : The TRIZ Methodology, Tools and Case Studies / / edited by Sebastian Koziołek, Leonid Chechurin, Mikael Collan |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (241 pages) |
| Disciplina | 153.43 |
| Soggetto topico |
Project management
Quality control Reliability Industrial safety Operations research Decision making Production management Project Management Quality Control, Reliability, Safety and Risk Operations Research/Decision Theory Production |
| ISBN | 3-319-96532-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Digital Learning Design: From Ideation via TRIZ to Implementation -- Experimental Validation of Quantum-Economic Analysis (QEA) as a Screening Tool for New Product Development -- Can Altshuller's Matrix be Skipped Using CBR and Semantic Similarity Reasoning? -- A Critical Comparison of Two Creativity Methods for Fostering Participatory Innovation: Implications to Improve TRIZ -- Problem Formulation of Screw Feeding System of Fibrous Materials Using TRIZ -- Quantifying and Leading Innovation with TRIZ Within Competitiveness Strategies -- TRIZ to Support Disruptive Innovation of Shared Bikes in China -- From Simulation to Contradictions, Different Ways to Formulate Innovation Directions -- How Problems Are Solved in TRIZ Literature: The Need for Alternative Techniques to Individuate the Most Suitable Inventive Principles -- TRIZ to Support Creation of Innovative Shared Value Business Initiatives -- Mobile Biogas Station Design - the TRIZ Approach -- Cause-Effect Chains Analysis Using Boolean Algebra -- A Praxiological Model of Creative Actions in the Field of Mechanical Engineering -- A Long-Term Strategy to Spread TRIZ in SMEs. Analysis of Bergamo's Experience -- Lessons for TRIZ from Design Thinking & Lean 3P -- TRIZ Potential for IT Projects -- TRIZ/CrePS Approach to the Social Problems of Poverty: 'Liberty vs. Love' Is Found the Principal Contradiction of the Human Culture -- Product Development Using Heuristic-Systematic Approach: A Case Study -- TRIZ Based Problem Solving of Tile Manufacturing System -- TRIZ-Based Approach for Process Intensification and Problem Solving in Process Engineering: Concepts and Research Agenda -- Problem Definition and Identification of Contradictions in the Interdisciplinary Areas of Mechatronic Engineering. |
| Record Nr. | UNINA-9910298188903321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Arte della scena e problem solving : la comunicazione persuasiva: psicoterapia, aziende, organizzazioni / Matteo Rampin
| Arte della scena e problem solving : la comunicazione persuasiva: psicoterapia, aziende, organizzazioni / Matteo Rampin |
| Autore | Rampin, Matteo |
| Pubbl/distr/stampa | Milano : R. Cortina, 2005 |
| Descrizione fisica | X, 240 p. : ill. ; 21 cm |
| Disciplina | 153.43 |
| Collana | Psicologia |
| Soggetto topico |
Recitazione - Aspetti psicologici
Psicoterapia - Metodi Comunicazione (Psicologia) Persuasione Soluzione di problemi |
| ISBN | 9788838628382 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | ita |
| Record Nr. | UNISALENTO-991000233739707536 |
Rampin, Matteo
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| Milano : R. Cortina, 2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. del Salento | ||
| ||
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 | ||
| ||
Case-Based Reasoning Research and Development [[electronic resource] ] : 27th International Conference, ICCBR 2019, Otzenhausen, Germany, September 8–12, 2019, Proceedings / / edited by Kerstin Bach, Cindy Marling
| Case-Based Reasoning Research and Development [[electronic resource] ] : 27th International Conference, ICCBR 2019, Otzenhausen, Germany, September 8–12, 2019, Proceedings / / edited by Kerstin Bach, Cindy Marling |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XXI, 405 p. 158 illus., 88 illus. in color.) |
| Disciplina | 153.43 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Application software Information storage and retrieval Data mining Artificial Intelligence Computer Appl. in Administrative Data Processing Information Storage and Retrieval Data Mining and Knowledge Discovery |
| ISBN | 3-030-29249-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Comparing Similarity Learning with Taxonomies and One-Mode Projection in Context of the FEATURE-TAK Framework -- An Algorithm Independent Case-Based Explanation Approach for Recommender Systems Using Interaction Graphs -- Explanation of Recommender Systems using Formal Concept Analysis -- FLEA-CBR { A Flexible Alternative to the Classic 4R Cycle of Lazy Learned Screening for Efficient Recruitment -- Case-Based Reasoning -- On the Generalization Capabilities of Sharp Minima in Case-Based Reasoning -- CBR Confidence as a Basis for Confidence in Black Box Systems -- Probabilistic Selection of Case-Based Explanations in an Underwater Mine Clearance Domain -- A Data-Driven Approach for Determining Weights in Global Similarity Functions -- Personalized case-based explanation of matrix factorization Recommendations -- How Case-Based Reasoning Explains Neural Networks -- Predicting Grass Growth for Sustainable Dairy Farming: A CBR System Using Bayesian Case-Exclusion and Post-Hoc, Personalized Explanation-by-Example (XAI) -- Learning Workflow Embeddings to Improve the Performance of Similarity-Based Retrieval for Process-Oriented Case-Based Reasoning -- On Combining Case Adaptation Rules -- Semantic Textual Similarity Measures for Case-Based Retrieval of Argument Graphs -- An approach to case-based reasoning based on local enrichment of the case base -- Improving analogical extrapolation using case pair competence -- Towards Finding Flow in Tetris -- Scoring Performance on the Y-Balance Test -- An Optimal Case-base Maintenance Method for Compositional Adaptation Applications -- Towards Human-like Bots using Online Interactive Case-Based Reasoning -- Show me your friends, I'll tell you who you are: Recommending products based on hidden evidence -- A Tale of Two Communities: An Analysis of Three Decades of Case-Based Reasoning Research -- Going Further with Cases: Using Case-Based Reasoning to Recommend Pacing Strategies for Ultra-Marathon Runners -- NOD-CC: A Hybrid CBR-CNN Architecture for Novel Object Discovery -- Adaptation of Scientific Workflows by Means of Process-Oriented Case-Based Reasoning. |
| Record Nr. | UNINA-9910349301403321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Case-Based Reasoning Research and Development [[electronic resource] ] : 27th International Conference, ICCBR 2019, Otzenhausen, Germany, September 8–12, 2019, Proceedings / / edited by Kerstin Bach, Cindy Marling
| Case-Based Reasoning Research and Development [[electronic resource] ] : 27th International Conference, ICCBR 2019, Otzenhausen, Germany, September 8–12, 2019, Proceedings / / edited by Kerstin Bach, Cindy Marling |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XXI, 405 p. 158 illus., 88 illus. in color.) |
| Disciplina | 153.43 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Application software Information storage and retrieval Data mining Artificial Intelligence Computer Appl. in Administrative Data Processing Information Storage and Retrieval Data Mining and Knowledge Discovery |
| ISBN | 3-030-29249-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Comparing Similarity Learning with Taxonomies and One-Mode Projection in Context of the FEATURE-TAK Framework -- An Algorithm Independent Case-Based Explanation Approach for Recommender Systems Using Interaction Graphs -- Explanation of Recommender Systems using Formal Concept Analysis -- FLEA-CBR { A Flexible Alternative to the Classic 4R Cycle of Lazy Learned Screening for Efficient Recruitment -- Case-Based Reasoning -- On the Generalization Capabilities of Sharp Minima in Case-Based Reasoning -- CBR Confidence as a Basis for Confidence in Black Box Systems -- Probabilistic Selection of Case-Based Explanations in an Underwater Mine Clearance Domain -- A Data-Driven Approach for Determining Weights in Global Similarity Functions -- Personalized case-based explanation of matrix factorization Recommendations -- How Case-Based Reasoning Explains Neural Networks -- Predicting Grass Growth for Sustainable Dairy Farming: A CBR System Using Bayesian Case-Exclusion and Post-Hoc, Personalized Explanation-by-Example (XAI) -- Learning Workflow Embeddings to Improve the Performance of Similarity-Based Retrieval for Process-Oriented Case-Based Reasoning -- On Combining Case Adaptation Rules -- Semantic Textual Similarity Measures for Case-Based Retrieval of Argument Graphs -- An approach to case-based reasoning based on local enrichment of the case base -- Improving analogical extrapolation using case pair competence -- Towards Finding Flow in Tetris -- Scoring Performance on the Y-Balance Test -- An Optimal Case-base Maintenance Method for Compositional Adaptation Applications -- Towards Human-like Bots using Online Interactive Case-Based Reasoning -- Show me your friends, I'll tell you who you are: Recommending products based on hidden evidence -- A Tale of Two Communities: An Analysis of Three Decades of Case-Based Reasoning Research -- Going Further with Cases: Using Case-Based Reasoning to Recommend Pacing Strategies for Ultra-Marathon Runners -- NOD-CC: A Hybrid CBR-CNN Architecture for Novel Object Discovery -- Adaptation of Scientific Workflows by Means of Process-Oriented Case-Based Reasoning. |
| Record Nr. | UNISA-996466443103316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Case-Based Reasoning Research and Development [[electronic resource] ] : 26th International Conference, ICCBR 2018, Stockholm, Sweden, July 9-12, 2018, Proceedings / / edited by Michael T. Cox, Peter Funk, Shahina Begum
| Case-Based Reasoning Research and Development [[electronic resource] ] : 26th International Conference, ICCBR 2018, Stockholm, Sweden, July 9-12, 2018, Proceedings / / edited by Michael T. Cox, Peter Funk, Shahina Begum |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (XIV, 628 p. 221 illus.) |
| Disciplina | 153.43 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Application software Information storage and retrieval User interfaces (Computer systems) Mathematical logic Special purpose computers Artificial Intelligence Computer Appl. in Administrative Data Processing Information Storage and Retrieval User Interfaces and Human Computer Interaction Mathematical Logic and Formal Languages Special Purpose and Application-Based Systems |
| ISBN | 3-030-01081-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910349402103321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Case-Based Reasoning Research and Development [[electronic resource] ] : 26th International Conference, ICCBR 2018, Stockholm, Sweden, July 9-12, 2018, Proceedings / / edited by Michael T. Cox, Peter Funk, Shahina Begum
| Case-Based Reasoning Research and Development [[electronic resource] ] : 26th International Conference, ICCBR 2018, Stockholm, Sweden, July 9-12, 2018, Proceedings / / edited by Michael T. Cox, Peter Funk, Shahina Begum |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (XIV, 628 p. 221 illus.) |
| Disciplina | 153.43 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Application software Information storage and retrieval User interfaces (Computer systems) Mathematical logic Special purpose computers Artificial Intelligence Computer Appl. in Administrative Data Processing Information Storage and Retrieval User Interfaces and Human Computer Interaction Mathematical Logic and Formal Languages Special Purpose and Application-Based Systems |
| ISBN | 3-030-01081-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996466325103316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Case-Based Reasoning Research and Development [[electronic resource] ] : 24th International Conference, ICCBR 2016, Atlanta, GA, USA, October 31 - November 2, 2016, Proceedings / / edited by Ashok Goel, M Belén Díaz-Agudo, Thomas Roth-Berghofer
| Case-Based Reasoning Research and Development [[electronic resource] ] : 24th International Conference, ICCBR 2016, Atlanta, GA, USA, October 31 - November 2, 2016, Proceedings / / edited by Ashok Goel, M Belén Díaz-Agudo, Thomas Roth-Berghofer |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (XI, 446 p. 123 illus.) |
| Disciplina | 153.43 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Information storage and retrieval Data mining Application software User interfaces (Computer systems) Computer communication systems Artificial Intelligence Information Storage and Retrieval Data Mining and Knowledge Discovery Information Systems Applications (incl. Internet) User Interfaces and Human Computer Interaction Computer Communication Networks |
| ISBN | 3-319-47096-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Searching Museum Routes using CBR,- Comparative Evaluation of Rule-Based and Case-Based Retrieval Coordination for Search of Architectural Building Designs,- Case Representation and Similarity Assessment in the selfBACK Decision Support System,- Accessibility-driven cooking system,- Inferring Users' Critiquing Feedback on Recommendations from Eye Movements -- Eager to be lazy: Towards a Complexity-guided Textual Case-Based Reasoning System -- Personalized Opinion-based Recommendation -- Concept Discovery and Argument Bundles in the Experience Web -- Incorporating Transparency During Trust-Guided Behavior Adaptation -- Inferring Student Coding Goals Using Abstract Syntax Trees -- Combining CBR and Deep Learning to Generate Surprising Recipe Designs -- Qualitative Case-based Reasoning for Humanoid Robot Soccer: a new retrieval and reuse algorithm -- Ensemble of Adaptations for Classification: Learning Adaptation Rules for Categorical features -- Similarity Metrics from Social Network Analysis for Content Recommender Systems -- Analogical Transfer in RDFS, Application to Cocktail Name Adaptation -- Adaptation-Guided Feature Deletion: Testing Recoverability to Guide Case Compression -- Applicability of Case-based Reasoning for Selection of Cyanide-free Gold Leaching Methods -- Competence Guided Casebase Maintenance for Compositional Adaptation Applications -- On the Transferability of Process-oriented Cases -- Case Completion of Workows for Process-Oriented Case-Based Reasoning -- Refinement-based Similarity Measures for Directed Labeled Graphs -- FEATURE-TAK - Framework for Extraction, Analysis, and Transformation of Unstructured Textual Aircraft Knowledge -- Knowledge Extraction and Annotation for Cross-Domain Textual Case Based Reasoning in Biologically Inspired Design -- Predicting the Electricity Consumption of Buildings: An Improved CBR Approach -- Case Representation and Retrieval Techniques for Neuroanatomical Connectivity Extraction from PubMed -- Compositional Adaptation of Explanations in Textual Case-based Reasoning -- Relevance Matrix Generation using Sensitivity Analysis in a Case-Based Reasoning Environment -- Combining Case-Based Reasoning and Reinforcement Learning for Tactical Unit Selection in Real-Time Strategy Game AI. |
| Record Nr. | UNINA-9910483450103321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Case-Based Reasoning Research and Development [[electronic resource] ] : 24th International Conference, ICCBR 2016, Atlanta, GA, USA, October 31 - November 2, 2016, Proceedings / / edited by Ashok Goel, M Belén Díaz-Agudo, Thomas Roth-Berghofer
| Case-Based Reasoning Research and Development [[electronic resource] ] : 24th International Conference, ICCBR 2016, Atlanta, GA, USA, October 31 - November 2, 2016, Proceedings / / edited by Ashok Goel, M Belén Díaz-Agudo, Thomas Roth-Berghofer |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (XI, 446 p. 123 illus.) |
| Disciplina | 153.43 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Information storage and retrieval Data mining Application software User interfaces (Computer systems) Computer communication systems Artificial Intelligence Information Storage and Retrieval Data Mining and Knowledge Discovery Information Systems Applications (incl. Internet) User Interfaces and Human Computer Interaction Computer Communication Networks |
| ISBN | 3-319-47096-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Searching Museum Routes using CBR,- Comparative Evaluation of Rule-Based and Case-Based Retrieval Coordination for Search of Architectural Building Designs,- Case Representation and Similarity Assessment in the selfBACK Decision Support System,- Accessibility-driven cooking system,- Inferring Users' Critiquing Feedback on Recommendations from Eye Movements -- Eager to be lazy: Towards a Complexity-guided Textual Case-Based Reasoning System -- Personalized Opinion-based Recommendation -- Concept Discovery and Argument Bundles in the Experience Web -- Incorporating Transparency During Trust-Guided Behavior Adaptation -- Inferring Student Coding Goals Using Abstract Syntax Trees -- Combining CBR and Deep Learning to Generate Surprising Recipe Designs -- Qualitative Case-based Reasoning for Humanoid Robot Soccer: a new retrieval and reuse algorithm -- Ensemble of Adaptations for Classification: Learning Adaptation Rules for Categorical features -- Similarity Metrics from Social Network Analysis for Content Recommender Systems -- Analogical Transfer in RDFS, Application to Cocktail Name Adaptation -- Adaptation-Guided Feature Deletion: Testing Recoverability to Guide Case Compression -- Applicability of Case-based Reasoning for Selection of Cyanide-free Gold Leaching Methods -- Competence Guided Casebase Maintenance for Compositional Adaptation Applications -- On the Transferability of Process-oriented Cases -- Case Completion of Workows for Process-Oriented Case-Based Reasoning -- Refinement-based Similarity Measures for Directed Labeled Graphs -- FEATURE-TAK - Framework for Extraction, Analysis, and Transformation of Unstructured Textual Aircraft Knowledge -- Knowledge Extraction and Annotation for Cross-Domain Textual Case Based Reasoning in Biologically Inspired Design -- Predicting the Electricity Consumption of Buildings: An Improved CBR Approach -- Case Representation and Retrieval Techniques for Neuroanatomical Connectivity Extraction from PubMed -- Compositional Adaptation of Explanations in Textual Case-based Reasoning -- Relevance Matrix Generation using Sensitivity Analysis in a Case-Based Reasoning Environment -- Combining Case-Based Reasoning and Reinforcement Learning for Tactical Unit Selection in Real-Time Strategy Game AI. |
| Record Nr. | UNISA-996465654803316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
