Artificial Intelligence Books

This book provides, in one volume, one of the best introductions to prolog programming and one of the best introductions to natural language processing. It is virtually unique in that it shows how to interface a natural language interface to a theorem prover.

This book was far ahead of its time when it was published, and it is still far ahead of most of the books written today on NLP.

Lots of areas of math, CS, and information science have recently
required a severe reorientation because of the emergence of computers that do not rely on classical physics, but rather derive an added "power" and speed from *quantum* effects, such as superposition and entanglement. Core areas of math go into
the new trend of applications. This exciting little book is well written and selfcontained. It gives an insight into this relatively new science, and it explains the math that is used,-- and how it is used. A great book for a course, or for self-study! Caution: There is a distinction betwee quantum computation and *quantum information*. While there are many books on the former, this attractive new book is on the latter, and it *is* a lovely introduction;- a very friendly invitation to the subject.

As well as featuring an extremely large and diverse collection of papers on the latest in Agent based technologies, this book has a wonderful introduction by the Editors. If you are considering, or indulging in, researching agent software then you need this book.

This book covers the essence of expert systems, and the use of expert systems supported by databases. It is well written and does not use excessive computerize to discuss the various topics. Easy to read and very easy to understand.

The aim of the book is to bring together key papers in Machine Learning and to provide an introduction to the field and a reference collection for graduate students and researchers. The book contains 51 most imoportant article from Machine Learning (up to 1990). Most of these are NOT available online, so watch out! The following areas are covered: Introduction (3 papers; one by Simon), Inductive Learning From Preclassified Training Examples (16 papers including great classics from Quinlan, Michalski, Mitchell, Minsky...), Unsupervided Learning and Concept Discovery (9 papers -- Feigenbaum, Holland...), Improving the Efiiciency of a Problem Solver (10 papers including fameous Samuel's gem "Some Studies in Machine Learning Using the Game of Checkers"; also papers from Mitchell, Nillson, Utgoff...), Using Preexisting Domain Knowledge Inductively (13 papers; Russel, etc...). Really really outstanding collection and a definite recommendation.

I was pleased to come across this book during my research for my master's thesis on music and artificial intelligence. Although there are a number of books on the subject, they tend to be collections of previously published articles or conference papers and are intended for an audience that already knows the subject of AI fairly well. This book, however, is not a compilation of articles, but a collection of chapters by various authors who were commissioned to write new material for the book. The result is a book with good introductory material in the field of AI and music, covering composition, analysis, knowledge representation, connectionism, and even music education/intelligent tutoring systems. At the end of each chapter, there is a bibliography pointing the reader towards more sources on that particular topic. I also think this would make a good textbook for an introductory class on AI and music for undergraduates (upper division) and graduate students that would appeal to all majors: theory and composition, musicology, and music education (and perhaps to a lesser extent, performance majors). I only wish this book had been available when I first began my research for my thesis - it would have made life a lot simpler! I highly recommend this book as the first book for any reader interested in this subject.

I have used only part of the book. Most of the book is explained using the 'muddy children' example, which also serves to illustrate the complexities in automating a trivial puzzle for humans. The book also contains accessible coverage of Kripke structures and Aumann diagrams. This is quite a accessible introduction to a fairly complex subject.

The book provides many aspects of logic programming in a clear and highly comprehensive manner. Though it is a monograph and deals with many advanced issues, the presentation is lucid and the readers need not pay too much effort to understand the concepts. The overview of the disciplines of logic programming have been presented in sufficient details in the first two chapters. The subsequent chapters mostly cover the original work by the authors and some comparative study with other eminent researchers like Reiter and Moore.The last two chapters provide some applications of the theories. I recommend the book for both the beginners and researchers working in this discipline.

Psychologists who think behaviorism has little or nothing to offer to a scientific account of cognition and emotion should read this book. Through coherent, conceptually pure theory and consistent empirical research, Hayes and colleagues have developed an account of these pivotal topics that may bring behaviorism back onto the main stage in psychology. Relational Frame Theory (RFT) makes a small handful of parsimonious additions to traditional Skinnerian radical behaviorism that appear to account for an impressively broad variety of clinical, social, and educational phenomena. RFT builds on the traditional strengths of behaviorism by bringing a small, core set of directly observable principles to bear on broad-ranging topics like language, cognition, and emotion. While Skinner's (1957) account of verbal behavior arguably minimized the importance of cognition and emotion, RFT recognizes their pivotal importance and points the way toward some novel and clever psychological interventions (most notably, Hayes Acceptance and Commitment Therapy, which is firmly grounded in RFT principles). In the process, RFT avoids the tenuous inferences and mechanism-postulating pitfalls of (for example) cognitive and psychodynamic theory, and avoids the scientific progress-retarding inconsistencies of theoretically eclectic approaches like cognitive-behaviorism. The biggest question that remains for the viability of an RFT approach to language, cognition, and emotion is: Is there predictive and influential utility in the approach? That is, does thinking about psychological and educational issues from an RFT perspective result in increasingly effective interventions? The answer to this question should unfold, empirically, over the next decade or two. This book is not for the casual reader-while RFT is at heart an elegantly simple set of principles, it is initially difficult to get one's head around the concept. But for psychologists & other social scientists with an abiding interest in solid scientific accounts of language, cognition, and emotion, this book is well worth the read. Coherent conceptual accounts based on good empirical data, like RFT, are very few and far between in psychology-and, collectively, are the best argument for psychology being classifiable as a science I have seen.

But why not write one more page? It just left me wanting more. And the surprise ending was my favorite part -- but I won't ruin it for other readers (hint -- it involves a watermelon.)