Readings in Knowledge Acquisition and Learning: Automating the Construction and Improvement of Programs

Readings in Knowledge Acquisition and Learning collects the best of the artificial intelligence literature from the fields of machine learning and knowledge acquisition. This book brings together for the first time the perspectives on constructing knowledge-based systems from these two historically separate subfields of artificial intelligence. A key criterion for article inclusion is an empirical demonstration that the method described in the paper successfully automates some important aspect of creating and maintaining a knowledge-based system. In addition to the papers, the editors provide an introduction to the field and to each group of papers, discussing their significance and pointing to related work. This book can serve as a text for courses and seminars in artificial intelligence, expert systems, knowledge acquisition, knowledge engineering, and machine learning. It will also provide practical ideas for professionals engaged in the building and maintenance of knowledge-based systems.

Table of Contents:
Chapter 1 Overview of Knowledge Acquisition and Learning 1.1 Overviews 1.1.1 R. S. Michalski Toward a unified theory of learning: Multistrategy task-adaptive learning 1.1.2 J. H. Boose A survey of knowledge acquisition techniques and tools Chapter 2 Expertise and Expert Systems 2.1 Expertise and Its Acquisition 2.1.1 J. R. Anderson Development of expertise 2.1.2 M. L. G. Shaw and J. B. Woodward Modeling expert knowledge 2.1.3 B. J. Wielinga, A. Th. Schreiber, & J. A. Breuker KADS: A modelling approach to knowledge engineering 2.1.4 D. E. Forsythe and B. G. Buchanan Knowledge acquisition for expert systems: Some pitfalls and suggestions 2.2 Expert Systems and Generic Problem Classes 2.2.1 B. G. Buchanan and R. G. Smith Fundamentals of expert systems 2.2.2 J. McDermott Preliminary steps toward a taxonomy of problem-solving methods 2.2.3 B. Chandrasekaran Generic tasks in knowledge-based reasoning: High-level building blocks for expert system design 2.2.4 W. J. Clancey Acquiring, representing, and evaluating a competence model of diagnostic strategy Chapter 3 Interactive Elicitation Tools 3.1 Eliciting Classification Knowledge 3.1.1 R. Davis Interactive transfer of expertise: Acquisition of new inference rules 3.1.2 J. H. Boose and J. M. Bradshaw Expertise transfer and complex problems: Using AQUINAS as a knowledge-acquisition workbench for knowledge-based systems 3.1.3 L. Eshelman, D. Ehret, J. McDermott, and M. Tan MOLE: A tenacious knowledge-acquisition tool 3.2 Eliciting Design Knowledge 3.2.1. S. Marcus and J. McDermott SALT: A knowledge acquisition language for propose-and-revise systems 3.2.2 M. A. Musen Automated support for building and extending expert models 3.2.3 T. R. Gruber Automated knowledge aquisition for strategic knowledge Chapter 4 Inductive Generalization Methods 4.1 Learning Classification Knowledge 4.1.1 R. S. Michalski A theory and methodology of inductive learning 4.1.2 J. R. Quinlan Induction of decision trees 4.1.3 G. E. Hinton Connectionist learning procedures 4.1.4. A. Ginsberg, S. M. Weiss, and P. Politakis Automatic knowledge base refinement for classification systems 4.2 Learning Classes Via Clustering 4.2.1 J. H. Gennari, P. Langley, and D. Fisher Models of incremental concept formation 4.2.2 P. Cheeseman, J. Kelly, M. Self, J. Stutz, W. Taylor, and D. Freeman Autoclass: A Bayesian classification system 4.3 Measurement and Evaluation of Learning Systems 4.3.1 J. W. Shavlik, R. Mooney, and G. G. Towell Symbolic and neural learning algorithms: An experimental comparison 4.3.2 B. R. Gaines The quantification of knowledge: Formal foundations for knowledge acquisition methodologies 4.3.3 T. G. Dietterich Limitations on inductive learning Chapter 5 Compilation and Deep Models 5.1 Compilation of Knowledge for Efficiency 5.1.1 R. E. Fikes, P. E. Hart, and N. J. Nilsson Learning and executing generalized robot plans 5.1.2 T. M. Mitchell, P. E. Utgoff, and R. Banerji Learning by experimentation: Acquiring and refining problem-solving heuristics 5.1.3 J. E. Laird, P. S. Rosenbloom, and A. Newell Chunking in SOAR: The anatomy of a general learning mechanism 5.2 Explanation-Based Learning of Classification Knowledge 5.2.1 T. M. Mitchell, R. M. Keller, S. T. Kedar-Cabelli Explanation-based generalization: A unifying view 5.2.2 R. J. Mooney Explanation generalization in EGGS 5.3 Synthesizing Problem Solvers from Deep Models 5.3.1 W. R. Swartout XPLAIN: A system for creating and explaining expert consulting programs 5.3.2 D. R. Barstow Domain-specific automatic programming 5.3.3 I. Bratko Qualitative modelling and learning in KARDIO Chapter 6 Apprenticeship Learning Systems 6.1 Apprentice Systems for Classification Knowledge 6.1.1 D. C. Wilkins Knowledge base refinement as improving an incomplete and incorrect domain theory 6.2 Apprentice Systems for Design Knowledge 6.2.1 T. M. Mitchell, S. Mabadevan, L. I. Steinberg LEAP: A learning apprentice for VLSI design 6.2.2 Y. Kodratoff and G. Tecuci Techniques of design and DISCIPLE learning apprentice Chapter 7 Analogical and Case-Based Reasoning 7.1 Analogical Reasoning 7.1.1 D. Gentner The mechanisms of analogical learning 7.1.2 B. Falkenhainer, K. D. Forbus, and D. Gentner The structure-mapping engine: Algorithm and examples 7.1.3 J. G. Carbonell Derivational analogy: A theory of reconstructive problem solving and expertise acquisition 7.2 Case-Based Reasoning 7.2.1 B. W. Porter, R. Bareiss, and R. C. Holte Concept learning and heuristic classification in weak-theory domains 7.2.2 A. R. Golding and P. S. Rosenbloom Improving rule-based systems through case-based reasoning 7.2.3 K. J. Hammond Explaining and repairing plans that fail Chapter 8 Discovery and Commonsense Reasoning 8.1 Discovery Learning 8.1.1 D. B. Lenat The ubiquity of discovery 8.1.2 L. B. Booker, D. E. Goldberg, and J. H. Holland Classifier systems and genetic algorithms 8.2 Commonsense Knowledge 8.2.1 R. Guha and D. B. Lenat CYC: A midterm report Bibliography Author Index Subject Index