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Most research in explanation-based learning involves relaxing constraints on the variables in the explanation of a specific example, rather than generalizing the structure of the explanation itself. However, this precludes the acquisition of concepts where an iterative process is implicitly represented in the explanation by a fixed number of applications. Such explanations must be reformulated during generalization. The fully-implemented BAGGER system analyzes explanation structures and detects extendible repeated, inter-dependent applications of rules. When any are found, the explanation is extended so that an arbitrary number of repeated applications of the original rule are supported. The final structure is then generalized and a new rule produced which embodies a crucial shift in representation. An important property of the extended rules is that their preconditions are expressed in terms of the initial state-they do not depend on the results of intermediate applications of the original rule. BAGGER's generalization algorithm is presented and empirical results that demonstrate the value of generalizing to N are reported. To illustrate the approach, the acquisition of a plan for building towers of arbitrary height is discussed in detail. Keywords: Artificial intelligence, Machine learning, Explanation-based learning, Empirical analysis.
Machine Learning: An Artificial Intelligence Approach, Volume III presents a sample of machine learning research representative of the period between 1986 and 1989. The book is organized into six parts. Part One introduces some general issues in the field of machine learning. Part Two presents some new developments in the area of empirical learning methods, such as flexible learning concepts, the Protos learning apprentice system, and the WITT system, which implements a form of conceptual clustering. Part Three gives an account of various analytical learning methods and how analytic learning can be applied to various specific problems. Part Four describes efforts to integrate different learning strategies. These include the UNIMEM system, which empirically discovers similarities among examples; and the DISCIPLE multistrategy system, which is capable of learning with imperfect background knowledge. Part Five provides an overview of research in the area of subsymbolic learning methods. Part Six presents two types of formal approaches to machine learning. The first is an improvement over Mitchell's version space method; the second technique deals with the learning problem faced by a robot in an unfamiliar, deterministic, finite-state environment.
Machine learning is a relatively new branch of artificial intelligence. The field has undergone a significant period of growth in the 1990s, with many new areas of research and development being explored.
Mathematics of Computing -- General.