Algorithmic Learning Theory: 9th International Conference, ALT’98 Otzenhausen, Germany, October 8–10, 1998 ProceedingsAuthor: Michael M. Richter, Carl H. Smith, Rolf Wiehagen, Thomas Zeugmann Published by Springer Berlin Heidelberg ISBN: 978-3-540-65013-3 DOI: 10.1007/3-540-49730-7Table of Contents:Editors’ Introduction Scalability Issues in Inductive Logic Programming Learning to Win Process-Control Games Watching Game-Masters Closedness Properties in EX-Identification of Recursive Functions Lower Bounds for the Complexity of Learning Half-Spaces with Membership Queries Cryptographic Limitations on Parallelizing Membership and Equivalence Queries with Applications to Random Self-Reductions Learning Unary Output Two-Tape Automata from Multiplicity and Equivalence Queries Computational Aspects of Parallel Attribute-Efficient Learning PAC Learning from Positive Statistical Queries Structured Weight-Based Prediction Algorithms Learning from Entailment of Logic Programs with Local Variables Logical Aspects of Several Bottom-Up Fittings Learnability of Translations from Positive Examples Analysis of Case-Based Representability of Boolean Functions by Monotone Theory Locality, Reversibility, and Beyond: Learning Languages from Positive Data Synthesizing Learners Tolerating Computable Noisy Data Characteristic Sets for Unions of Regular Pattern Languages and Compactness Finding a One-Variable Pattern from Incomplete Data A Fast Algorithm for Discovering Optimal String Patterns in Large Text Databases A Comparison of Identification Criteria for Inductive Inference of Recursive Real-Valued Functions, Includes bibliographical references and index