Abstracting background knowledge for concept learning -- A multistrategy learning approach to domain modeling and knowledge acquisition -- Using plausible explanations to bias empirical generalization in weak theory domains -- The replication problem: A constructive induction approach -- Integrating an explanation-based learning mechanism into a general problem-solver -- Analytical negative generalization and empirical negative generalization are not cumulative: A case study -- Evaluating and changing representation in concept acquisition -- Application of empirical discovery in knowledge acquisition -- Using accuracy in scientific discovery -- KBG : A generator of knowledge bases -- On estimating probabilities in tree pruning -- Rule induction with CN2: Some recent improvements -- On changing continuous attributes into ordered discrete attributes -- A method for inductive cost optimization -- When does overfitting decrease prediction accuracy in induced decision trees and rule sets? -- Semi-naive bayesian classifier -- Description contrasting in incremental concept formation -- System FLORA: Learning from time-varying training sets -- Message-based bucket brigade: An algorithm for the apportionment of credit problem -- Acquiring object-knowledge for learning systems -- Learning nonrecursive definitions of relations with linus -- Extending explanation-based generalization by abstraction operators -- Static learning for an adaptative theorem prover -- Explanation-based generalization and constraint propagation with interval labels -- Learning by explanation of failures -- PANEL : Logic and learnability -- Panel on : Causality and learning -- Seed space and version space: Generalizing from approximations -- Integrating EBL with automatic text analysis -- Abduction for explanation-based learning -- Consistent term mappings, term partitions, and inverse resolution -- Learning by analogical replay in prodigy: First results -- Analogical reasoning for logic programming -- Case-based learning of strategic knowledge -- Learning in distributed systems and multi-agent environments -- Learning to relate terms in a multiple agent environment -- Extending learning to multiple agents: Issues and a model for multi-agent machine learning (MA-ML) -- Applications of machine learning: Notes from the panel members -- Evaluation of learning systems : An artificial data-based approach -- Shift of bias in learning from drug compounds: The fleming project -- Learning features by experimentation in chess -- Representation and induction of musical structures for computer assisted composition -- IPSA: Inductive protein structure analysis -- Four stances on knowledge acquisition and machine learning -- Programme of EWSL-91.