Case-Based Reasoning Research and Development
Description
The 26 revised full papers presented were carefully reviewed and selected from 37 submissions. The papers cover a wide range of CBR topics that are of interest both to researchers and practitioners from foundations of Case-Based Reasoning; over CBR systems for specific tasks and related fields; up to CBR systems, applications and lessons learned in specific areas of expertise such as health; e-science; finance; energy, logistics, traffic; game/AI; cooking; diagnosis, technical support; as well as knowledge and experience management
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Content
Case Base Maintenance in Preference-based CBR.- Learning to Estimate: A Case-Based Approach to Task Execution Prediction .- Case-based Policy and Goal Recognition.- Adapting Sentiments with Context.- Aspect Selection for Social Recommender Systems.- Music Recommendation: Audio Neighbourhoods to Discover Music in the Long Tail.- Goal-Driven Autonomy with Semantically-annotated Hierarchical Cases.- Evaluating a Textual Adaptation System.- Visual Case Retrieval for Interpreting Skill Demonstrations.- Improving Trust-Guided Behavior Adaptation Using Operator Feedback.- Top-Down Induction of Similarity Measures Using Similarity Clouds.- Improving Case Retrieval Using Typicality.- CBR Meets Big Data: A Case Study of Large-Scale Adaptation Rule Generation.- Addressing the Cold-Start Problem in Facial Expression Recognition.- Flexible Feature Deletion: Compacting Case Bases by Selectively Compressing Case Contents.- A Case-Based Approach For Easing Schema Semantic Mapping.- Great Explanations: Opinionated Explanations for Recommendation.- Learning and Applying Adaptation Operators in Process-Oriented Case-Based Reasoning.- Fault Diagnosis via Fusion of Information from a Case Stream.- Argument-based Case Revision in CBR for Story Generation.- CBR Model for Predicting a Building's Electricity Use: On-Line Implementation in the Absence of Historical Data.- Modelling Hierarchical Relationships in Group Recommender Systems.- Semi-automatic Knowledge Extraction from Semi-structured andUnstructured Data within the OMAHA Project.- Evidence-Driven Retrieval in Textual CBR: Bridging the Gap Between Retrieval and Reuse.- Maintaining and Analyzing Production Process Definitions Using a Tree-Based Similarity Measure.- Case-Based Plan Recognition Under Imperfect Observability.
