1986 | | | **Explanation-Based Learning: An Alternative View** - *Gerald Dejong and Raymond Mooney* |

| | | **Inductive Inference Hierarchies: Probabilistic vs Pluralistic** - *R. P. Daley* |

| | | **Probability and Measure** - *Patrick Billingsley* |

| | | **Chemical Discovery as Belief Revision** - *Donald Rose and Pat Langley* |

| | | **Editorial: Human and Machine Learning** - *Pat Langley* |

| | | **News and Notes first of 86** - *Thomas G. Dietterich* |

| | | **Induction of Decision Trees** - *J. R. Quinlan* |

| | | **Integrating Quantitative and Qualitative Discovery: The ABACUS System** - *Brian C. Falkenhainer and Ryszard S. Michalski* |

| | | **Systems That Learn** - *D. Osherson, M. Stob and S. Weinstein* |

| | | **Parallel Distributed Processing Volume I: Foundations** - *D. E. Rumelhart and J. L. McClelland* |

| | | **Editorial: The Terminology of Machine Learning** - *Pat Langley* |

| | | **Learning Distributed Representations of Concepts** - *G. E. Hinton* |

| | | **Understanding the Nature of Learning: Issues and Research Directions** - *R. M. Michalski* |

| | | **On the Inference of Programs Approximately Computing the Desired Function** - *C. Smith and M. Velauthapillai* |

| | | **Studies on Inductive Inference from Positive Data** - *T. Shinohara* |

| | | **Determining Arguments of Invariant Functional Descriptions** - *Mieczyslaw M. Kokar* |

| | | **Learning at the Knowledge Level** - *Thomas G. Dietterich* |

| | | **Stochastic Complexity and Modeling** - *J. Rissanen* |

| | | **Chunking in Soar: The Anatomy of a General Learning Mechanism** - *John E. Laird, Paul S. Rosenbloom and Allen Newell* |

| | | **An Algebraic Framework for Inductive Program Synthesis** - *K. P. Jantke* |

| | | **The disjunctive Learning Problem** - *M. Fulk* |

| | | **Machine Learning of Nearly Minimal Size Grammars** - *J. Case and H. Chi* |

| | | **On Barzdin’s Conjecture** - *T. Zeugmann* |

| | | **A General Framework for Parallel Distributed Processing** - *D. E. Rumelhart, G. E. Hinton and J. L. McClelland* |

| | | **Machine Learning: An Artificial Intelligence Approach 2** - *R. S. Michalski and J. G. Carbonell and T. M. Mitchell* |

| | | **On the Complexity of Inductive Inference** - *R. Daley and C. Smith* |

| | | **Using telltales in developing program test sets** - *J. Cherniavsky and C. Smith* |

| | | **A General Framework for Induction and a Study of Selective Induction** - *Larry Rendell* |

| | | **On The Inference of Sequences of Functions** - *W. Gasarch and C. Smith* |

| | | **Inductive inference by refinement** - *P. Laird* |

| | | **Distributional Expectations and the Induction of Category Structure** - *M. J. Flannagan, L. S. Fried and K. J. Holyoak* |

| | | **Inductive Inference of Functions From Noised Observations** - *J. Grabowski* |

| | | **Learning Concepts by Asking Questions** - *C. Sammut and R. Banerji* |

| | | **News and Notes** - *Thomas G. Dietterich, Nicholas S. Flann and David C. Wilkins* |

| | | **Experimental Goal Regression: A Method for Learning Problem-Solving Heuristics** - *Bruce W. Porter and Dennis F. Kibler* |

| | | **Machine Learning and Discovery** - *Pat Langley and Ryszard S. Michalski* |

| | | **Incremental Learning from Noisy Data** - *Jeffrey C. Schlimmer and Jr. Richard H. Granger* |

| | | **Linear Function Neurons: Structure and Training** - *S. E. Hampson and D. J. Volper* |

| | | **A Theory of Historical Discovery: The Construction of Componential Models** - *Jan M. Zytkow and Herbert A. Simon* |

| | | **Explanation-Based Generalization: A Unifying View** - *Tom M. Mitchell, Richard M. Keller and Smadar T. Kedar-Cabelli* |

| | | **Aggregating Inductive Expertise** - *D. Osherson, M. Stob and S. Weinstein* |

| | | **A Framework for Empirical Discovery** - *P. Langley and B. Nordhausen* |

| | | **Inductive Inference of approximations** - *J. Royer* |

| | | **A General Theory of Discrimination Learning** - *P. Langley* |

| | | **On the Inductive Inference of Programs with Anomalies** - *M. Velauthapillai* |

| | | **News and Notes** - *Yves Kodratoff, Gheorghes Tecuci and Thomas G. Dietterich* |

| | | **On Machine Learning** - *Pat Langley* |

| | | **Learning Representations By Back-Propagating Errors** - *D. E. Rumelhart, G. E. Hinton and R. J. Williams* |

| | | **Some Problems on Inductive Inference from Positive Data** - *T. Shinohara* |

| | | **Learning Internal Representations by Error Propagation** - *D. E. Rumelhart, G. E. Hinton and R. J. Williams* |

| | | **A Theory and Methodology of Inductive Inference** - *R. S. Michalski* |

| | | **Learning Machines** - *J. Case* |

| | | **Identification in the Limit of First Order Structures** - *D. N. Osherson and S. Weinstein* |

| | | **Parallel Distributed Processing - Explorations in the Microstructure of Cognition** - *J. L. McClelland, D. E. Rumelhart and t. P. R. Group* |

| | | **Stochastic Complexity and Sufficient Statistics** - *J. Rissanen* |

| | | **How Fast is Program Synthesis from Examples** - *R. Wiehagen* |

| | | **Some Results in the Theory of Effective Program Synthesis - Learning by Defective Information** - *G. SchÃ¤fer-Richter* |

| | | **An Analysis of a Learning Paradigm** - *D. Osherson, M. Stob and S. Weinstein* |

| | | **On the Complexity of Effective Program Synthesis** - *R. Wiehagen* |

| | | **Systems that Learn: An Introduction to Learning Theory for Cognitive and Computer Scientists** - *D. N. Osherson, M. Stob and S. Weinstein* |

| | | **Stratified Inductive Hypothesis Generation** - *Z. S. Szabo* |

| | | **Stochastic Relaxation Methods for Image Restoration and Expert Systems** - *S. Geman* |

| | | **Machine Learning of Inductive Bias** - *P. E. Utgoff* |

| | | **On the Complexity of Program Synthesis from Examples** - *R. Wiehagen* |

| | | **Learning from positive-only examples** - *R. Berwick* |

| | | **Towards the Development of an Analysis of Learning Algorithms** - *R. Daley* |

| | | **The Effect of Noise on Concept Learning** - *J. R. Quinlan* |

| January | | **An Introduction to Hidden Markov Models** - *L. R. Rabiner and B. H. Juang* |

| February | | **Genetic AI-Translating Piaget into Lisp** - *G. L. Drescher* |

| May | | **On the Logic of Representing Dependencies by Graphs** - *J. Pearl and A. Paz* |

| | | **CONSENSUS: A Statistical Learning Procedure in a Connectionist Network** - *G. J. Goetsch* |

| June | | **Types of queries for concept learning** - *D. Angluin* |

| | | **A Lemma on the Multiarmed Bandit Problem** - *J. N. Tsitsiklis* |

| October | | **Analogical and Inductive Inference, International Workshop AII ’86. Wendisch-Rietz, GDR** - *K. P. Jantke* |