Department of Computer Science


Readings

A good place for starting Python is actually the on-line Python tutorial. The Python language is available for Windows, Mac and Linux/Unix.

Books (required)

Russell, S., and Norvig, P., (2002). Artificial Intelligence: A Modern Approach. Prentice Hall.

Books (recommended)

Sutton, R. S., and Barto, A. G., (1998). Reinforcement Learning. Cambridge, Mass.: MIT Press.

Pearl, J., (1984). Heuristics: Intelligent Search Strategies for Computer Problem-Solving. Addison Wesley. A very interesting and unconventional book discussing a large number of interesting questions and deep issues connected with heuristics used in classical AI. Some prior experience with AI search methods is probably useful before reading this book.

Pearl, J., (2000). Causality: Models, Reasoning and Inference. Cambridge, UK: Cambridge University Press.
Luger, G., and Stubblefield, W., (1997). Artificial Intelligence: Structures and Strategies for Complex Problem Solving. Addison-Wesley. A modern approach.

Further reading suggestions

Lungarella, M., Iida, F., Bongard, J., and Pfeifer, R., editors, Proc. 50th Anniversary Summit of Artificial Intelligence. Springer-Verlag: Berlin, Heidelberg, New York.

Saygin, A., Cicekli, I., and Akman, V., (2000). Turing Test: 50 Years Later. Minds and Machines, 10(4):463-518.

Hofstadter, D., (1999). Gödel Escher Bach: An Eternal Golden Braid. Basic Books Inc. 20th edition.

Nilsson, N., (1998). AI: A New Synthesis. Morgan Kauffman. A new book from one of the most important classical AI authors.

Selected readings from Proceedings of Conferences and Series: International Joint Conference on Artificial Intelligence (IJCAI); American Association for Artificial Intelligence (AAAI); Artificial Intelligence and Simulation of Behaviour (AISB); European Conference on Artificial Intelligence (ECAI); Springer Lecture Notes on Artificial Intelligence (LNAI).

Papers and other Material

William Freeman, e.g. "Constructing Free Energy Approximations and Generalized Belief Propagation Algorithms"

Jack Breese, Daphne Koller: "Bayesian Networks and Decision-Theoretic Reasoning for Artificial Intelligence"

Research at the University of Hertfordshire

Check out our work on Artificial Intelligence, Artificial Life and Information Theory for Intelligent Information Processing

Home (Theory and Practice of Artificial Intelligence)



Last changed at Mon Feb 6 12:41:48 2017 by D. Polani