Taught: Spring 2010
Grambling State University, Louisiana, USA
Description
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. We will define the concept of knowledge representation (the core of Artificial Intelligence). We finally cover the uncertain knowledge (using a probability measure), and the learning machine paradigm (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.
Textbook
D. Poole, A. Mackworth, and R. Goebel, Computational Intelligence: A Logical Approach, Oxford University Press, January 1998
Powerpoint
Course Materials
Chapter 1
Chapter 2-3 (part 1) (*)
Chapter 2-3 (part 2)
Chapter 4 (part 1)
Chapter 4 (part 2)
Chapter 5
Chapter 6
Chapter 7
Chapter 10 (part 1)
Chapter 10 (part 2)
Chapter 10 (part 3)
Chapter 11 (part 1)
Chapter 11 (part 2)
Chapter 12
(*) Chapter 2 & 3 are presented together as they form a coherent whole. They are separate in the book to keep formalisms & the methodology separate.