AI
Common Lisp Artificial Intelligence tools by topic:

The CMU Artificial Intelligence Repository contains a wealth of both Lisp and AI (as well as Scheme and Prolog) related documents and code.

AI Books

  • Emperical Methods for Artificial Intelligence Paul R. Cohen; MIT Press; ISBN 0-262-03225-2 Computer science and artificial intelligence in particular have no curriculum in research methods, as other sciences do. This book presents empirical methods for studying complex computer programs: exploratory tools to help find patterns in data, experiment designs and hypothesis-testing tools to help data speak convincingly, and modeling tools to help explain data. Although many of these techniques are statistical, the book discusses statistics in the context of the broader empirical enterprise. The first three chapters introduce empirical questions, exploratory data analysis, and experiment design. The blunt interrogation of statistical hypothesis testing is postponed until chapters 4 and 5, which present classical parametric methods and computer-intensive (Monte Carlo) resampling methods, respectively. This is one of few books to present these new, flexible resampling techniques in an accurate, accessible manner. Much of the book is devoted to research strategies and tactics, introducing new methods in the context of case studies. Chapter 6 covers performance assessment, chapter 7 shows how to identify interactions and dependencies among several factors that explain performance, and chapter 8 discusses predictive models of programs, including causal models. The final chapter asks what counts as a theory in AI, and how empirical methods -- whiping cch deal with specific systems -- can foster general theories.

  • The Limits of Mathematics, The Unknowable G. J. Chaitin; Springer-Verlag Singapore; 1997; ISBN 981-3083-59-X; ISBN 1-85233-668-4. The Limits of Mathematics presents the final version of Chaitin's course on the limits of mathematical reasoning. This course uses algorithmic information theory to show that mathematics has serious limitations, and features a new more didactic approach to algorithmic information theory using LISP and Mathematica software. The thesis of the book is that the incompleteness phenomenon discovered by Goedel is much more widespread and serious than hitherto suspected. Also Goedel and Einstein's views on the foundations of mathematics are discussed, and it is suggested that mathematics is quasi-empirical and that experimental mathematics should be used more freely. [Publisher's Abstract] The companion "prequel" volume, The Unknowable Springer-Verlag Singapore; 1999; ISBN: 981-4021-72-5; presents an accessible historical survey contrasting Chaitin's ideas on the limits and structure of mathematical understanding with Goedel's concept of incompleteness and Turing's concept of uncomputability.

  • LISP 3rd edition; Patrick H. Winston, Berthold K. P. Horn; Addison-Wesley (Reading, MA); 1989; ISBN 0-201-08319-1 Covers the basic concepts of the language, but also gives a lot of detail about programming AI topics such as rule-based expert systems, forward chaining, interpreting transition trees, compiling transition trees, object oriented programming, and finding patterns in images.

  • The Elements of Artificial Intelligence Using Common Lisp 2nd edition; Steven Tanimoto; Computer Science Press; 1995; ISBN 0-71-67826-9-3; ISBN 0-71-67823-0-8

  • Common LISP Modules: Artificial Intelligence in the Era of Neural Networks and Chaos Theory Mark Watson; Springer-Verlag; 1991; ISBN 0-387-97614-0

  • Natural language understanding James Allen; 1994; Addison-Wesley; ISBN 0-8053-0334-0

  • Genetic Programming: On the Programming of Computers by Means of Natural Selection John Koza; 1992; MIT Press

  • Genetic Programming II: Automatic Discovery of Reusable Programs John Koza; 1994; MIT Press

  • Books on Artificial Intelligence and Mathematics, with a Lisp language emphasis; ALU list dead link, redirected to archive.org

  • Building Problem Solvers describes how to build reasoning systems, using Common Lisp source code available at this web site. The code includes a variety of rule engines, truth maintenance systems, and constraint systems.

  • Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp by Peter Norvig.

  • Natural Language Processing in Lisp ­ An Introduction to Computational Linguistics Gerald Gazdar, Chris Mellish; 1989; Addison-Wesley Publishing Company; ISBN 0-201-17825-7

  • Common Lisp Programming for Artificial Intelligence Tony Hasemer, John Domingue; 1989; Addison-Wesley Publishing Company; ISBN 0-201-17579-7 This book presents an introduction to Artificial Intelligence with an emphasis on the role of knowledge representation. Three chapters focus on object-oriented programming, including the construction and use of a subset of CLOS. The authors' research into the problems faced by novice Lisp users influenced the content and style of the book. (The authors are members of the Human Cognition Research Laboratory at the Open University in the United Kingdom.) The book employs a tutorial approach, especially in areas that students often find difficult, such as recursion. Early and progressive treatment of the evaluator promotes understanding of program execution. Hands-on exercises are used to reinforce basic concepts. The book assumes no prior knowledge of Lisp or AI and is a suitable textbook for students in Cognitive Science, Computer Science and other disciplines taking courses in Lisp or AI programming as well as being invaluable for professional programmers who are learning Lisp for developing AI applications.

  • Artificial Intelligence with Common Lisp ­ Fundamental of Symbolic and Numeric Processing James L. Noyes; 1992; D.C.Heath and Company; ISBN 978-0-669-19473-9; ISBN 0-669-19473-5

  • Artificial Intelligence Programming Eugene Charniak, Christopher K. Riesbeck, Drew V. McDermott, James R. Meehon; 1987; Lawrence Erlbaum Associates Publishers; ISBN 0-89859-609-2