Case-Based Reasoning Research and Development: First International Conference, ICCBR-95, Sesimbra, Portugal, October 23 - 26, 1995. Proceedings(Lecture Notes in Artificial Intelligence)

This book constitutes the refereed proceedings of the First International Conference on Case-Based Reasoning, ICCBR-95, held in Sesimbra, Portugal, in October 1995. The 52 revised papers included are classified as scientific papers , application papers , and posters . All current aspects of research and development aiming at industrial applications in CBR are addressed. Among the topical sections are case and knowledge representation, case retrieval, nearest neighbour methods, case adaption and learning, cognitive modelling, integrated reasoning methods, and application-oriented methods: planning, decision making, diagnosis, interpretation, design, etc.

Table of Contents:
Integration of case based retrieval with a relational database system in Aircraft Technical Support.- Cost estimation of software projects through case base reasoning.- Operator decision aiding by adaptation of supervision strategies.- PROFIL: A decision support tool for metallic sections design using a CBR approach.- MacRad: Radiology image resource with a case-based retrieval system.- Representing and indexing building refurbishment cases for multiple retrieval of adaptable pieces of cases.- Large-scale fault diagnosis for on-board train systems.- Case-based reasoning for expertise relocation in support of rural health workers in developing countries.- Spatial composition using cases: IDIOM.- CBR and Machine Learning for combustion system design.- KBS maintenance as learning two-tiered domain representation.- A case-based approach for developing writing tools aimed at non-native English users.- Reasoning with reasons in case-based comparisons.- Towards the integration of case-based, schema-based and model-based reasoning for supporting complex design tasks.- Separating the cases from the data: Towards more flexible case-based reasoning.- Route planning by analogy.- Case adaptation using an incomplete causal model.- Evaluating the application of CBR in mesh design for simulation problems.- Case memory and retrieval based on the immune system.- Using case data to improve on rule-based function approximation.- Learning to improve case adaptation by introspective reasoning and CBR.- Retrieving cases in structured domains by using goal dependencies.- An average-case analysis of k-nearest neighbor classifier.- Cases as terms: A feature term approach to the structured representation of cases.- ADAPtER: An integrated diagnostic system combining case-based and abductive reasoning.- Adaptation using constraint satisfaction techniques.- Learning a local similarity metric for case-based reasoning.- Experiments on adaptation-guided retrieval in case-based design.- Integrating rules and cases for the classification task.- Reuse of knowledge: Empirical studies.- Weighting features.- An investigation of marker-passing algorithms for analogue retrieval.- INRECA: A seamlessly integrated system based on inductive inference and case-based reasoning.- DOM-ArC: An active decision support system for quality assessment of cases.- A case-based reasoner adaptive to different cognitive tasks.- On the use of CBR in optimisation problems such as the TSP.- Case-based diagnosis of multiple faults.- On the automatic generation of case libraries by chunking chess games.- Learning to refine indexing by introspective reasoning.- Problem solving with “the incredible machine” an experiment in case-based reasoning.- Integrating case based reasoning and tabu search for solving optimisation problems.- Systems, tasks and adaptation knowledge: Revealing some revealing dependencies.- Some limitations of feature-based recognition in case-based design.- A case based method for solving relatively stable dynamic constraint satisfaction problems.- Learning strategies for explanation patterns: Basic game patterns with application to chess.- A memory-based hierarchical planner.- Case-Based Reasoning for cash flow forecasting using fuzzy retrieval.- A connectionist indexing approach for CBR systems.- Using a neural network to learn general knowledge in a case-based system.- “Fish and Sink” an anytime-algorithm to retrieve adequate cases.- Knowledge engineering for CBR systems from a cognitive science perspective.- Towards using a single uniform metric in instance-basedlearning.