Advances in Case-Based Reasoning

1. Front Matter

   Pages I-XII

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2. Invited Papers

   1. Integrating Background Knowledge into Nearest-Neighbor Text Classification

      • Sarah Zelikovitz, Haym Hirsh

      Pages 1-5

   2. Applying Knowledge Management: Techniques for Building Organisational Memories

      • Ian Watson

      Pages 6-12

3. Research Papers

   1. On the Complexity of Plan Adaptation by Derivational Analogy in a Universal Classical Planning Framework

      • Tsz-Chiu Au, Héctor Muñoz-Avila, Dana S. Nau

      Pages 13-27

   2. Inductive Learning for Case-Based Diagnosis with Multiple Faults

      • Joachim Baumeister, Martin Atzmüller, Frank Puppe

      Pages 28-42

   3. Diverse Product Recommendations Using an Expressive Language for Case Retrieval

      • Derek Bridge, Alex Ferguson

      Pages 43-57

   4. Digital Image Similarity for Geo-spatial Knowledge Management

      • James D. Carswell, David C. Wilson, Michela Bertolotto

      Pages 58-72

   5. Poetry Generation in COLIBRI

      • Belén Díaz-Agudo, Pablo Gervás, Pedro A. González-Calero

      Pages 73-87

   6. Adaptation Using Iterated Estimations

      • Göran Falkman

      Pages 88-102

   7. The Use of a Uniform Declarative Model in 3D Visualisation for Case-Based Reasoning

      • Göran Falkman

      Pages 103-117

   8. Experiments on Case-Based Retrieval of Software Designs

      • Paulo Gomes, Francisco C. Pereira, Paulo Paiva, Nuno Seco, Paulo Carreiro, José L. Ferreira et al.

      Pages 118-132

   9. Exploiting Taxonomic and Causal Relations in Conversational Case Retrieval

      • Kalyan Moy Gupta, David W. Aha, Nabil Sandhu

      Pages 133-147

   10. Bayesian Case Reconstruction

       • Daniel N. Hennessy, Bruce G. Buchanan, John M. Rosenberg

       Pages 148-158

   11. Relations between Customer Requirements, Performance Measures, and General Case Properties for Case Base Maintenance

       • Ioannis Iglezakis, Thomas Reinartz

       Pages 159-173

   12. Representing Temporal Knowledge for Case-Based Prediction

       • Martha Dørum Jære, Agnar Aamodt, Pål Skalle

       Pages 174-188

   13. Local Predictions for Case-Based Plan Recognition

       • Boris Kerkez, Michael T. Cox

       Pages 189-203

   14. Automatically Selecting Strategies for Multi-Case-Base Reasoning

       • David B. Leake, Raja Sooriamurthi

       Pages 204-218

   15. Diversity-Conscious Retrieval

       • David McSherry

       Pages 219-233

   16. Improving Case Representation and Case Base Maintenance in Recommender Agents

       • Miquel Montaner, Beatriz López, Josep Lluís de la Rosa

       Pages 234-248

   17. Similarity Assessment for Generalizied Cases by Optimization Methods

       • Babak Mougouie, Ralph Bergmann

       Pages 249-263

The papers collected in this volume were presented at the 6th European C- ference on Case-Based Reasoning (ECCBR 2002) held at The Robert Gordon University in Aberdeen, UK. This conference followed a series of very succe- ful well-established biennial European workshops held in Trento, Italy (2000), Dublin, Ireland (1998), Lausanne, Switzerland (1996), and Paris, France (1994), after the initial workshop in Kaiserslautern, Germany (1993). These meetings have a history of attracting ?rst-class European and international researchers and practitioners in the years interleaving with the biennial international co- terpart ICCBR; the 4th ICCBR Conference was held in Vancouver, Canada in 2001. Proceedings of ECCBR and ICCBR conferences are traditionally published by Springer-Verlag in their LNAI series. Case-Based Reasoning (CBR) is an AI problem-solving approach where pr- lems are solved by retrieving and reusing solutions from similar, previously solved problems, and possibly revising the retrieved solution to re?ect di?erences - tween the new and retrieved problems. Case knowledge stores the previously solved problems and is the main knowledge source of a CBR system. A main focus of CBR research is the representation, acquisition and maintenance of case knowledge. Recently other knowledge sources have been recognized as important: indexing, similarity and adaptation knowledge. Signi?cant knowledge engine- ing e?ort may be needed for these, and so the representation, acquisition and maintenance of CBR knowledge more generally have become important.

• AI Planning
• CBR
• Case-Based Making
• Case-Based Management
• Case-Based Problem Solving
• Case-Based Reasoning
• Case-Based Reasoning Systems,
• Fuzzy
• Knowledge Engineering
• Optimization Methods
• cognition
• complexity
• organization
• proving
• recommender system

• School of Computing, The Robert Gordon University, Aberdeen, Scotland, UK

  Susan Craw

• Computing Science Department, University of Aberdeen, Aberdeen, Scotland, UK

  Alun Preece

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