About the Course

This course will cover several advanced topics in Artificial Intelligence beyond those covered in CS580. These topics will extend existing knowledge about search, machine learning, reasoning, and situated action. Some topics are required; others may be negotiated with the class. Topics may include planning, probabilistic reasoning, reinforcement learning, evolutionary computation, advanced neural networks, natural language processing, constraint satisfaction, reactive systems, knowledge-based learning, robotics, vision, emergent behavior, and intelligent multiagent systems.

AI is a breadth-oriented field, and the goal of this course is to provide the student with sufficient breadth beyond CS580 to act as a well-versed AI researcher.

Any programming assignments, other than the final project, will be done in Common Lisp, the traditional exploratory programming language of AI. Ordinarily (but not always) Common Lisp is thoroughly taught in CS580. If not, we may spend time reviewing it and performing basic programming in it first, depending on the CS687 student population. The course may introduce other AI languages as well, including Prolog and Scheme.

The CS 795 course number is for PhD students only.

Further information will appear on the Course Web Page

Texts

	Artificial Intelligence: A Modern Approach SECOND edition, by Russell and Norvig. This book is green, not red.
	ANSI Common Lisp by Paul Graham, ISBN: 0133708756.

Grading Policies

This course will consist largely of several large projects and two exams. The breakdown will be approximately:

Homework and Projects	50% with higher weight given to harder projects
Exams	25% Each