EWSL 1987: Bled, Yugoslavia

Part One: Learning from Examples in the Presence of Noise

• Peter Clark, Tim Niblett: Induction in Noisy Domains. 11-30
• Bojan Cestnik, Igor Kononenko, Ivan Bratko: ASSISTANT 86: A Knowledge-Elicitation Tool for Sophisticated Users. 31-45
• Matjaz Gams, Nada Lavrac: Review of Five Empirical Learning Systems Within a Proposed Schemata. 46-66
• Tim Niblett: Constructing Decision Trees in Noisy Domains. 67-78
• Christopher J. C. H. Watkins: Combining Cross-Validation and Search. 79-87

Part Two: Meta-Knowledge and Explanation in Learning

• Yves Kodratoff: Is AI a Sub-Field of Computer Science - or is AI the Science of Explanations? 91-106
• Roberto Desimone: Learning Control Knowledge Within an Explanation-Based Learning Framework. 107-120
• Jean-Francois Puget: Goal Regression with Opponent. 121-137
• Pavel Brazdil: Knowledge States and Meta-Knowledge Maintenance. 138-146

Part Three: Studies of Special Mechanisms for Learning

• Nicolas Helft: Inductive Generalization: A Logical Framework. 149-157
• Jean-Gabriel Ganascia: Learning with Hilbert Cubes. 158-171
• Jiarong Hong, Carl T. Uhrik: The Extension Matrix Approach to Attribute Based Learning. 172-182
• Michel Manago, Yves Kodratoff: Model Driven Learning of Disjunctive Concepts. 183-198

Part Four: Learning in Complex Domains

• Igor Mozetic: Learning of Qualitative Models. 201-217
• Stephen Muggleton: Structuring Knowledge by Asking Questions. 218-229
• Ross D. King: An Inductive Learning Approach to the Problem of Predicting a Protein's Secondary Structure from Its Amino Acid Sequence. 230-250