Taught: Winter 2002, Winter 2004, Winter 2006, Winter 2007
We introduce this course by defining the concept of Computational Intelligence. We first introduce the idea of agents in the world. We then discuss the notion of representation and reasoning system and provide some case studies that illustrate this concept. Different search algorithms such as graph-searching, blind search, heuristic search and constraint satisfaction problems will be covered. After defining the concept of knowledge representation (the core of Artificial Intelligence), we will tackle the knowledge engineering (knowledge-based systems, meta-interpreters) and symbolic knowledge (based on first-order predicate calculus, and modal logic). We finally cover the uncertain knowledge (using a probability measure), and some learning machine paradigms (learning as choosing the best representation and learning under uncertainty). We conclude the course by applying some of the artificial intelligence concepts in robotic systems.
D. Poole, A. Mackworth, and R. Goebel, Computational Intelligence: A Logical Approach, Oxford University Press, January 1998 (ISBN 0195102703)