EWSL 1991: Porto, Portugal

Part 1: Cronstructive Induction and Multi-strategy Approaches

• Attilio Giordana, Davide Roverso, Lorenza Saitta:
  Abstracting Background Knowledge for Concept Learning. 1-13
  
• Gheorghe Tecuci:
  A Multistrategy Learning Approach to Domain Modeling and Knowledge Acquisition. 14-32
  
• Gerhard Widmer:
  Using Plausible Explanations to Bias Empirical Generalizations in Weak Theory Domains. 33-43
  
• Der-Shung Yang, Gunnar Blix, Larry A. Rendell:
  The Replication Problem: A Constructive Induction Approach. 44-61
  
• F. Zerr, Jean-Gabriel Ganascia:
  Integrating an Explanation-Based Learning Mechanism into a General Problem-Solver. 62-80
  
• Claudio Carpineto:
  Analytical Negative Generalization and Empirical Negative Generalization are not Cumulative: A Case Study. 81-88
  
• Francesco Bergadano, Floriana Esposito, Céline Rouveirol, Stefan Wrobel:
  Panel: Evaluating and Changing Representation in Concept Acquisition. 89-100
  

Part 2: Discovery

• Jan M. Zytkow, Jieming Zhu:
  Panel: Application of Empirical Discovery in Knowledge Acquisition. 101-117
  
• Majorie Moulet:
  Using Accuracy in Scientific Discovery. 118-136
  
• Gilles Bisson:
  KBG: A Generator of Knowledge Bases. 137
  

Part 3: Numeric and Statistical Approaches

• Bojan Cestnik, Ivan Bratko:
  On Estimating Probabilities in Tree Pruning. 138-150
  
• Peter Clark, Robin Boswell:
  Rule Induction with CN2: Some Recent Improvements. 151-163
  
• Jason Catlett:
  On Changing Continuous Attributes into Ordered Discrete Attributes. 164-178
  
• Floor Verdenius:
  A Method for Inductive Cost Optimization. 179-191
  
• Cullen Schaffer:
  When Does Overfitting Decrease Prediction Accuracy in Induced Decision Trees and Rule Sets?. 192-205
  
• Igor Kononenko:
  Semi-Naive Bayesian Classifier. 206-219
  
• Christine Decaestecker:
  Description Contrasting in Incremental Concept Formation. 220-233
  
• Miroslav Kubat, Jirina Pavlickova:
  The System FLORA: Learning from Type-Varying Training Sets. 234
  
• Marco Dorigo:
  Message-Based Bucket Brigade: An Algorithm for the Apportionment of Credit Problem. 235-244
  

Part 4: Theorem Proving and EBL

• Luc De Raedt, Johan Feyaerts, Maurice Bruynooghe:
  Acquiring Object-Knowledge for Learning Systems. 245-264
  
• Nada Lavrac, Saso Dzeroski, Marko Grobelnik:
  Learning Nonrecursive Definitions of Relations with LINUS. 265-281
  
• Igor Mozetic, Christian Holzbaur:
  Extending Explanation-Based Generalization by Abstraction Operators. 282-297
  
• Catherine Belleannée, Jacques Nicolas:
  Static Learning for an Adaptive Theorem Prover. 298-311
  
• Kai Zercher:
  Explanation-Based Generalization and Constraint Propagation with Interval Labels. 312-326
  
• Paulo Urbano:
  Learning by Explanation of Failures. 327-343
  
• Luc De Raedt:
  Panel: Logic and Learnability. 344
  
• Lorenza Saitta:
  Panel: Causality and Learning. 345
• Jacques Nicolas:
  Seed Space and Version Space: Generalizing from Approximations. 346
  
• Sylvain Delisle, Stan Matwin, Lionel Zupan:
  Integrating EBL with automatic Text Analysis. 347
  

Part 5: Inversion of Resolution

• Béatrice Duval:
  Abduction and Induction for Explanation-Based Learning. 348-360
  
• Shan-Hwei Nienhuys-Cheng, Peter A. Flach:
  Consistent Term Mappings, Term Partitions and Inverse Resolution. 361-374
  

Part 6: Analogy and Case-Based Learning

• Manuela M. Veloso, Jaime G. Carbonell:
  Learning by Analogical Replay in PRODIGY: First Results. 375-390
  
• Birgit Tausend, Siegfried Bell:
  Analogical Reasoning for Logic Programming. 391-397
  
• Beatriz López, Enric Plaza:
  Case-Based Learning of Strategic Knowledge. 398-411
  

Part 7: Multi-agents

• Pavel Brazdil, Matjaz Gams, Sati S. Sian, Luís Torgo, Walter Van de Velde:
  Panel: Learning in Distributed Systems and Multi-Agent Environments. 412-423
  
• Pavel Brazdil, Stephen Muggleton:
  Learning to Relate Terms in a Multiple Agent Environment. 424-439
  
• Sati S. Sian:
  Extending Learning to Multiple Agents: Issues and a Model for Multi-Agent Machine Learning (MA-ML). 440-456
  

Part 8: Applications

• Peter Clark, Bojan Cestnik, Claude Sammut, Joachim Stender:
  Panel: Applications of Machine Learning: Notes from the Panel Members. 457-462
  
• Hakim Lounis, Gilles Bisson:
  Evaluation of Learning Systems: An Artificial Data-Based Approach. 463-481
  
• Luigi Di Pace, F. Fabrocini, G. Bolis:
  Shift of Bias in Learning from Drug Compounds: The Fleming Project. 482-493
  
• Eduardo F. Morales:
  Learning Features by Experimentation in Chess. 494-511
  
• Francis Courtot:
  Representation and Induction of Musical Structures for Computer Assisted Composition. 512
  
• Steffen Schulze-Kremer, Ross D. King:
  IPSA: Inductive Protein Structure Analysis. 513
  
• Thomas R. Addis, Yves Kodratoff, Ramon López de Mántaras, Katharina Morik, Enric Plaza:
  Panel: Four Stances on Knowledge Acquisition and Machine Learning. 514-533