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"This book consists of an organized description of "intelligent" machines. The book is primarily a textbook for undergraduate and graduate student s of computer science in general, and artificial intelligence in particular."--Preface
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.
Search is an important component of problem solving in artificial intelligence (AI) and, more generally, in computer science, engineering and operations research. Combinatorial optimization, decision analysis, game playing, learning, planning, pattern recognition, robotics and theorem proving are some of the areas in which search algbrithms playa key role. Less than a decade ago the conventional wisdom in artificial intelligence was that the best search algorithms had already been invented and the likelihood of finding new results in this area was very small. Since then many new insights and results have been obtained. For example, new algorithms for state space, AND/OR graph, and game tree search were discovered. Articles on new theoretical developments and experimental results on backtracking, heuristic search and constraint propaga tion were published. The relationships among various search and combinatorial algorithms in AI, Operations Research, and other fields were clarified. This volume brings together some of this recent work in a manner designed to be accessible to students and professionals interested in these new insights and developments.
While the development of Information Technology has been obvious to all, the underpinning computer science has been less apparent. Subrata Dasgupta provides a thought-provoking introduction to the field and its core principles, considering computer science as a science of symbol processing.
Can computers think? Can they use reason to develop their own concepts, solve complex problems, understand our languages? This updated edition of a comprehensive survey includes extensive new text on "Artificial Intelligence in the 21st Century," introducing deep neural networks, conceptual graphs, languages of thought, mental models, metacognition, economic prospects, and research toward human-level AI. Ideal for both lay readers and students of computer science, the original text features abundant illustrations, diagrams, and photographs as well as challenging exercises. Lucid, easy-to-read discussions examine problem-solving methods and representations, game playing, automated understanding of natural languages, heuristic search theory, robot systems, heuristic scene analysis, predicate-calculus theorem proving, automatic programming, and many other topics.
There are many books available in the market on the proposed topic but none of them can be termed as comprehensive. Besides, students face many problems in understanding the language of this books. Keeping these points in mind, Artificial Intelligence was prepared, which should be simple enough to comprehend and comprehensive enough to encompass all the topics of different institutions and universities.
The Handbook of Artificial Intelligence, Volume I focuses on the progress in artificial intelligence (AI) and its increasing applications, including parsing, grammars, and search methods. The book first elaborates on AI, AI handbook and literature, problem representation, search methods, and sample search programs. The text then ponders on representation of knowledge, including survey of representation techniques and representation schemes. The manuscript explores understanding natural languages, as well as machine translation, grammars, parsing, test generation, and natural language processing systems. The book also takes a look at understanding spoken language, including systems architecture and the ARPA SUR projects. The text is a valuable source of information for computer science experts and researchers interested in pursuing further research in artificial intelligence