Some of the many AI books available in Snell Library

CSU520 Artificial Intelligence - Spring 2009 - Professor Futrelle

version of 4 January 2009

All of the books listed below should be in the Snell Library collection. The course textbook, "AIMA", by Russell and Norvig, is the only one I've placed on Reserve.

Book citation Notes
Akmajian, A., Demers, R.A., Farmer, A.K. and Harnish, R.M.
Linguistics: An Introduction to Language and Communication.
The MIT Press, Cambridge, MA, 2001.
This is a popular and easy-to-read book on Linguistics. You can learn a lot from it, with little effort. Some of the topics such as phonology are not as relevant to AI as others (though they do relate to the important speech recognition problem).
Allen, J. Natural Language Understanding.
Benjamin/Cummings Publishing Company,
Menlo Park, CA, 1994.
This is a classic text on the topic. A bit dated, but quite a good book. It begins with a useful introduction to the syntax of natural language, before getting into the computational aspects.
Baclawski, K. and Niu, T.
Ontologies for bioinformatics.
MIT Press, Cambridge, Mass., 2006.
Ontologies for Bioinformatics is a hot topic these days, from gene identities and relations to terms in disease and treatment. The topic relates to Chapter 10 of the course textbook by Russell and Norvig, the AIMA textbook. The Protégé system that I will demonstrate, and you can use in your projects, is focused on developing ontologies. The first author is Professor Baclawski in our College.
Bekoff, M., Allen, C. and Burghardt, G.M. The cognitive animal: Empirical and theoretical perspectives on animal cognition.
MIT Press, Cambridge, Mass., 2002.
A collection of 57 short and easy-to-read chapters on animal cognition. In the 1970s it was rather unheard of to even speak of cognition in animals. The field has exploded since then, with fascinating new results and insights coming at a great clip.
Bly, B.M. and Rumelhart, D.E. Cognitive science.
Academic Press, San Diego, Calif., 1999.
A collection of eight chapters by a variety of authors. The chapters that are particularly relevant to our AI course include the ones on Categorization and Reasoning.
Brachman, R.J. and Levesque, H.J.
Knowledge representation and reasoning.
Elsevier/Morgan Kaufmann, San Francisco, CA, 2004.
This covers less topics than our AIMA textbook, but covers them in more detail. For example, it has more material on frames, structured descriptions, inheritance, and defaults.
Buckland, M. Programming game AI by example.
Wordware Pub., Plano, Texas, 2005.
This is a lengthy treatment of a number of important aspects of AI-based game programming. There is extensive C++ code in the book and it is all available for download at
Dale, R., Moisl, H., & Somers, H. L. . Handbook of natural language processing. New York: Marcel Dekker, 2000. This book has lengthier and more technical chapters than the Mitkov collection below.
Duda, R.O., Hart, P.E. and Stork, D.G. Pattern Classification.
Wiley, New York, 2001.
This is an excellent book that complements our AIMA text with material on statistical decision theories, maximum likelihood, linear discriminants, and an extensive chapter on neural networks.
Funge, J.D. Artificial intelligence for computer games: An introduction. Peters, Wellesley, Mass., 2004. This book has two things that make it quite useful and interesting for our AI course:
It is small and short, about 130 pages.
The concepts and terminology closely match our AIMA textbook.
Hastie, T., Tibshirani, R. and Friedman, J.H.
The elements of statistical learning: Data mining, inference, and prediction
Springer, New York, 2001.
This popular book is rather mathematical. It surveys many machine learning techniques. It has more of an emphasis on the statistical view than most books on the topic.
Isbister, K. Better game characters by design:
A psychological approach.
Elsevier/Morgan Kaufmann, Amsterdam ; Boston, 2006.
This book is interesting because of its focus on the psychology of game characters. It has numerous pictures, interviews with game designers, and a DVD with 48 QuickTime movie excerpts from games, all tied to discussions in the book. I will play some of the excerpts in class. Katherine Isbister is an Associate Professor at RPI, where she studies, HCI, especially as related to games.
Jurafsky, D. and Martin, J.H.
Speech and Natural Language Processing.
Prentice-Hall, Upper Saddle River, NJ, 2000.
This is a popular, up-to-date book on the subject. It describes the standard approaches of the current era, including, stemming, probabilistic models, N-grams, and probabilistic parsing.
Levitin, D.J. Foundations of cognitive psychology:
Core readings. MIT Press, Cambridge, Mass., 2002.
This is an 800+ page book that contains reprints of 39 important papers in the field, with 18 sections entitled: Foundations, Neural Networks, Experimental Design,Perception, Categories and Concepts, Memory, Attention, Human-Computer Interaction, Music Cognition, Expertise, Decision Making, Evolutionary Approaches, Language Acquisition, Language and Thought, Pragmatics, Intelligence, and Cognitive Neuroscience.
Manning, C.D. and Schütze, H.
Foundations of Statistical Natural Language Processing.
MIT Press, Cambridge, Massachusetts, 1999.
This is a well-known textbook that covers a wide variety of the statistical methods used in a variety of natural language analysis systems.
Medin, D.L., Ross, B.H. and Markman, A.B.
Cognitive psychology. John Wiley & Sons, Hoboken, NJ, 2005.
This textbook covers many of the same ideas as in our textbook, but from a complementary point of view. Every chapter has some material related to our AI course.
Millington, I. Artificial intelligence for games.
Elsevier: Amsterdam
Boston: Morgan Kaufmann,, 2006.
This is an extensive and authoritative book on the subject. It shows, in some detail, how a wide variety of well-known AI concepts and techniques can be used in designing games. Its approach to AI is solid and not watered down.
Mitchell, T.M. Machine Learning.
McGraw-Hill, New York, 1997.
Though a bit dated, this is still an excellent textbook on machine learning. It is not too long, but covers a wide range of important topics.
Mitkov, R. The Oxford handbook of computational linguistics. Oxford; New York: Oxford University Press, 2003.
Contains many short and readable chapters on a wide range of computational linguistics topics
Nilsson, N.J. Artificial Intelligence: A new synthesis.
Morgan Kaufmann Publishers, San Francisco, Calif., 1998.
This is a complete and rather recent textbook on artificial intelligence. It is half the length of the AIMA book and not as technically deep. So it is a good way to gain understanding of various topics in our course.
Norvig, P. Paradigms of Artificial Intelligence Programming:
Case Studies in Common Lisp.
Morgan Kaufmann Publishers, San Mateo, CA, 1992.
This is a fascinating book on how to actually write AI programs. The entire treatment is in Lisp. The book is so good, it's worth learning Lisp just to be able to read and enjoy it. See his site:
Norvig is currently (2009) the Director of Research at Google.
Russell, S.J. and Norvig, P.
Artificial intelligence: A modern approach.
Prentice Hall/Pearson Education, Upper Saddle River, N.J., 2003.
This book, "AIMA", is the textbook for the course. It is far and away the leading textbook in the field. The website is It is the only one I've placed on Reserve.
Segaran, T. Programming Collective Intelligence.
O'Reilly, 2007.
The book is subtitled: "Building Smart Web 2.0 Applications" It is distinguished from many other Web 2.0 books by the high level of its technical content, including discussions of machine learning, e.g., clustering, decision trees, neural networks, and support vector machines.
Skutch, A.F. The minds of birds.
Texas A&M University Press, College Station, 1996.
A delightful, non-technical, short, and easy-to-read book. It proves that the common meaning of the word "birdbrain" is far from accurate in describing the fascinating things that birds are able to do.
Stillings, N.A., Weisler, S.E., Chase, C.H.,
Feinstein, M.H., Garfield, J.L. and Rissland, E.L.
Cognitive science: An introduction.
MIT Press, Cambridge, Mass., 1995.
This is an approachable undergraduate textbook on the topic.
Thagard, P.
Mind: Introduction to cognitive science.
MIT Press, Cambridge, Mass., 2005.
A well-written, short and easy-to-read introduction to the field.
Weiss, S.M. and Kulikowski, C.A.
Computer Systems that Learn.
Morgan Kaufmann, San Mateo, CA, 1991.
Though this book is a bit old, it is short and well-written and gives a nice overview of the fundamental concepts of machine learning.
Witten, I.H. and Frank, E.
Data Mining: Practical Machine Learning Tools and Techniques.
Morgan Kaufmann, 2005.
This is an important and useful book for our course. It is built around the famous WEKA machine learning tools. The WEKA system has a variety of machine learning algorithms that you can apply and a variety of visual interfaces for examining the results. Weka is written in Java; you can download it and run it anywhere. I will demo WEKA in class. Last year, a number of students used WEKA in their projects.

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