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目次

Chapter 1 Overview of Knowledge Acquisition and Learning 1.1 Overviews1.1.1 R. S. Michalski Toward a unified theory of learning:Multistrategy task-adaptive learning 1.1.2 J. H. Boose A survey ofknowledge acquisition techniques and tools Chapter 2 Expertise andExpert Systems 2.1 Expertise and Its Acquisition 2.1.1 J. R. AndersonDevelopment of expertise 2.1.2 M. L. G. Shaw and J. B. WoodwardModeling 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 expertsystems: Some pitfalls and suggestions 2.2 Expert Systems and GenericProblem Classes 2.2.1 B. G. Buchanan and R. G. Smith Fundamentals ofexpert systems 2.2.2 J. McDermott Preliminary steps toward a taxonomyof problem-solving methods 2.2.3 B. Chandrasekaran Generic tasks inknowledge-based reasoning: High-level building blocks for expert systemdesign 2.2.4 W. J. Clancey Acquiring, representing, and evaluating acompetence model of diagnostic strategy Chapter 3 InteractiveElicitation Tools 3.1 Eliciting Classification Knowledge 3.1.1 R. DavisInteractive transfer of expertise: Acquisition of new inference rules3.1.2 J. H. Boose and J. M. Bradshaw Expertise transfer and complexproblems: Using AQUINAS as a knowledge-acquisition workbench forknowledge-based systems 3.1.3 L. Eshelman, D. Ehret, J. McDermott, andM. Tan MOLE: A tenacious knowledge-acquisition tool 3.2 ElicitingDesign Knowledge 3.2.1. S. Marcus and J. McDermott SALT: A knowledgeacquisition language for propose-and-revise systems 3.2.2 M. A. MusenAutomated support for building and extending expert models 3.2.3 T. R.Gruber Automated knowledge aquisition for strategic knowledge Chapter 4Inductive Generalization Methods 4.1 Learning Classification Knowledge4.1.1 R. S. Michalski A theory and methodology of inductive learning4.1.2 J. R. Quinlan Induction of decision trees 4.1.3 G. E. HintonConnectionist learning procedures 4.1.4. A. Ginsberg, S. M. Weiss, andP. Politakis Automatic knowledge base refinement for classificationsystems 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. FreemanAutoclass: A Bayesian classification system 4.3 Measurement andEvaluation of Learning Systems 4.3.1 J. W. Shavlik, R. Mooney, and G.G. Towell Symbolic and neural learning algorithms: An experimentalcomparison 4.3.2 B. R. Gaines The quantification of knowledge: Formalfoundations for knowledge acquisition methodologies 4.3.3 T. G.Dietterich Limitations on inductive learning Chapter 5 Compilation andDeep Models 5.1 Compilation of Knowledge for Efficiency 5.1.1 R. E.Fikes, P. E. Hart, and N. J. Nilsson Learning and executing generalizedrobot plans 5.1.2 T. M. Mitchell, P. E. Utgoff, and R. Banerji Learningby experimentation: Acquiring and refining problem-