Michalski R.S., Carbonell J.G., Mitchell T.M. (eds.) Machine Learning. An Artificial Intelligence Approach

Springer, 1983. — 565.The ability to learn is one of the most fundamental attributes of intelligent behavior. Consequently, progress in the theory and computer modeling of learning processes is of great significance to fields concerned with understanding intelligence. Such fields include cognitive science, artificial intelligence, information science, pattern recognition, psychology, education, epistemology, philosophy, and related disciplines.
The recent observance of the silver anniversary of artificial intelligence has been heralded by a surge of interest in machine learning-both in building models of human learning and in understanding how machines might be endowed with the ability to learn. This renewed interest has spawned many new research projects and resulted in an increase in related scientific activities. In the summer of 1980, the First Machine Learning Workshop was held at Carnegie-Mellon University in Pittsburgh. In the same year, three consecutive issues of the International Journal of Policy Analysis and Information Systems were specially devoted to machine learning (No.2, 3 and 4, 1980). In the spring of 1981, a special issue of the SIGART Newsletter No. 76 reviewed current research projects in the field.
This book contains tutorial overviews and research papers representative of contemporary trends in the area of machine learning as viewed from an artificial intelligence perspective. As the first available text on this subject, it is intended to fulfill several needs. For researchers in artificial intelligence, computer science, and cognitive psychology, it provides an easily accessible collection of state-of-the-art papers presenting current results, which will hopefully spur further research. For students in artificial intelligence and related disciplines, this volume may serve as a supplementary textbook for a course in artificial intelligence, or as a primary text for a specialized course devoted to machine learning. Finally, due to the potential impact of machine learning on a variety of disciplines, this book may be of interest to a diverse range of readers, including computer scientists, robotics experts, knowledge engineers, educators, philosophers, data analysts, psychologists and electronic engineers.Part One General Issues in Machine Learning
An Overview of Machine Learning
Why Should Machines Learn?
Part Two Learning from Examples
A Comparative Review of Selected Methods for Learning from Examples
A Theory and Methodology of Inductive Learning
Part Three Learning in Problem-Solving and Planning
Learning by Analogy: Formulating and Generalizing Plans from Past Experience
Learning by Experimentation: Acquiring and Refining Problem-Solving Heuristics
Acquisition of Proof Skills in Geometry
Using Proofs and Refutations to Learn from Experience
Part Four Learning from Observation And Discovery
The Role of Heuristics in Learning by Discovery: Three Case Studies
Rediscovering Chemistry With the BACON System
Learning From Observation: Conceptual Clustering
Part Five Learning from Instruction
Machine Transformation of Advice into a Heuristic Search Procedure
Learning by Being Told: Acquiring Knowledge for Information Management
The Instructible Production System: A Retrospective Analysis
Part Six Applied Learning Systems
Learning Efficient Classification Procedures and their Application to Chess End Games
Inferring Student Models for Intelligent Computer-Aided Instruction
Comprehensive Bibliography of Machine Learning