Intelligent Systems I:  2008-2009

To teach students the principal issues that arise in the theory, implementation and application of intelligent software systems. Here "intelligent systems" will be taken to mean those that have the ability to represent and reason about complex tasks given limited computational resources.

1. Fundamental Issues. Possibility of achieving intelligent behaviour; objections to intelligent systems; the Turing test; rational actions. [2]
2. Search. Problem solving as search; search strategies; informed search methods. [3]
3. Planning and scheduling. [3]
4. Introduction to probabilistic reasoning. Probability theory; Bayes' theorem; Bayesian Networks; Markov processes. [4]
5. Practical systems. Dealing with extent. Case studies. [3]

Fundamental issues in intelligent systems. Searching and planning. Introduction to probabilistic reasoning: Bayes rule, forward inference, and the Kalman filter. Dealing with uncertainty and geometry for physical agents.

• Bratko, Prolog Programming for Artificial Intelligence (3rd ed), Addison-Wesley, 2000.
• S.J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach (2nd ed), Prentice-Hall, 2001.