Some material for presentations

For most topics Russell-Norvig is the best starting point. You can also check books on machine learning and data mining. For example, Kantardzic is a very easy-reading, and covers decision trees, neural networks, fuzzy logic and genetic algorithms. Below you can find some other sources you can begin with. However, try to find also other material, so that you can explain how the system works, its advantages and disadvantages, current state (is it popular or not and why!) and applications in expert systems.

in Russell-Norvig:

Decision trees: ch 18.3
Rule-based systems: ch 7 (especially 7.1 and 7.7)
Default rules: ch 10 (especially 10.7 and 10.8)
Probabilistic reasoning: ch 14
Neural networks: ch 20.5

Fuzzy logic

Zadeh, L.A.: The role of fuzzy logic in the management of uncertainty in expert systems. Fuzzy sets and systems, 11, 1983. (A pioneering paper on fuzzy ESs.)
A tutorial
Fuzzy logic archive
Several links

Bayesian networks

Pearl and Russell: Bayesian networks (a short and easy-reading paper)
Judea Pearl's homepage (several publications!)
Pearl, J.: Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kauffmann, 1988. (The first book on Bayesian networks)

Truth-maintenance systems

Problem Solving and Truth Maintenance Systems (A brief overview)
Doyle, J.: A truth maintenance system. Artificial Intelligence, vol 12, num 3, Nov 1979. (The original paper, where idea of JTMS was first introduced.)
deKleer, J.: An assumption-based TMS. Artificial Intelligence, vol 28, num 2, Mar 1986. (The original paper on ATMS.)
deKleer, J.: Extending the ATMS. Artificial Intelligence, vol 28, num 2, Mar 1986. (Extensions to handle defaults and disjunctions of assumptions.)
Dubois, D., Lang, J. and Prade, H.: A possibilistic assumption-based truth maintenance system with uncertain justifications, and its application to belief revision. In Truth maintenance systems: ECAI-90 workshop, 1991. (An interesting extension of ATMS.)

Dempster-Shafer theory

Shafer, G.: Dempster-Shafer theory (A really brief introduction with some references!)
Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, 1976. The original book where theory was introduced.
Laskey, K.B. and Lehner, P.E.: Assumptions, beliefs and probabilities. Artificial Intelligence,... (Implementing Dempster-Shafer theory by ATMS.)

Neural networks

Duda, R.O., Hart, E. and Stark, D.G.: Pattern classification. 2. ed, John Wiley and Sons, 2001. (A new revised edition of an old classic!)
Ripley, B.D.: Pattern recognition and neural networks. Cambridge University Press, 1996.
Duda: Learned from neural networks A good evaluation of advantages and disadvantages.
A short easy-reading tutorial with some references
A more comprehensive tutorial
Several links

Support vector machines

Burges: A tutorial on Support Vector Machines for Pattern Recognition
Gunn: Support Vector Machines for Classification and Regression. See under this page
Several links
An application in fault diagnosis

Genetic algorithms

Mitchell: An introduction to genetic algorithms. MIT Press, 1996.
also a chapter in Mitchell: Machine Learning.
several publications by John H. Holland (make searches with keywor genetic algorithms)
Genetic algorithms archive
Several links

Case-based reasoning

Kolodner: Case-based reasoning. Morgan Kaufmann, 1993.
Aamodt and Plaza: Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches in A. Aamodt, E. Plaza (1994); AICom - Artificial Intelligence Communications, IOS Press, Vol. 7: 1, pp. 39-59.
Several links