Description: Inductive Logic Programming. Extensions of Inversion of Resolution Applied to Theory Completion. Generalization and Learnability: A Study of Constrained Atoms. Learning Theoretical Terms. Logic Program Synthesis from Good Examples. A Critical Comparison of Various Methods Based on Inverse Resolution. Non-Monotonic Learning. An Overview of the Interactive Concept-Learner and Theory Revisor CLINT. A Framework for Inductive Logic Programming. The Rule-Based Systems Project: Using Confirmation Theory and Non-Monotonic Logics for Incremental Learning. Relating Relational Learning Algorithms. Machine Invention of First-Order Predicates By Inverting Resolution. Efficient Induction of Logic Programs. Constraints for Predicate Invention. Refinement Graphs for FOIL and LINUS. Controlling the Complexity of Learning in Logic Through Syntactic and Task-Oriented Models. Efficient Learning of Logic Programs with Non-Determinate, Non-Discriminating Literals. An Information-Based Approach to Integrating Empirical and Explanation-Based Learning. Analogical Reasoning for Logic Programming. Some Thoughts on Inverse Resolution. Experiments in Non-Monotonic First-Order Induction. Learning Qualitative Models of Dynamic Systems. The Application of Inductive Logic Programming to Finite Element Mesh Design. Inducing Temporal Fault Diagnostic Rules from a Qualitative Model. Inductive Learning of Relations from Noisy Examples. Learning Chess Patterns. Applying Inductive Logic Programming in Reactive Environments. Chapter References.

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