The ability to learn is one of the most fundamental attributes of intelligent behavior. Consequently, progress in the theory and computer modeling of learn ing processes is of great significance to fields concerned with understanding in telligence. Such fields include cognitive science, artificial intelligence, infor mation 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 Inter national 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."

In recent years, machine learning has emerged as a significant area of research in artificial intelligence and cognitive science. At present, research in the field is being intensified from both the point of view of theory and of implementation, and the results are being introduced in practice. Machine learning has recently become the subject of interest of many young and talented scientists whose bold ideas have greatly contributed to the broadening of knowledge in this rapidly developing field of science. This situation has manifested itself in an increasing number of valuable contributions to scientific journals. However, such papers are necessarily compact descriptions of research problems. "Computational Models of Learning" supplements these contributions and is a collection of more extensive essays. These essays provide the reader with an increased knowledge of carefully selected problems of machine learning.