This is an advanced undergraduate course that covers advanced topics in Artificial Intelligence. Topics vary from one offering to the next and are likely to be drawn from the following list: planning, scheduling, games, search, reasoning (constraint-based, model-based, spatial, temporal), knowledge representation, decision-making under uncertainty, reinforcement learning, agents, foundations.

The content of the course will vary at each offering. In general, through this course, students should...

• Gain both a wide and a deep knowledge of the topic(s) taught in the current instance of the course.
• Improve their skills at navigating through, and critically examining, the scientific literature on the taught topic(s).

Assignments (45%); Seminar (15%); Final Exam (40%)

30 one hour lectures

Students are assumed to have solid background knowledge in general computer science (e.g., programming experience; some basic theoretical CS), but no specialist knowledge.

COMP3620

Marcus Hutter (2005) Universal Artificial Intelligence, EATCS, Springer.
http://www.springer.com/computer/ai/book/978-3-540-22139-5

Shane Legg (2008) Machine Super Intelligence, Lulu, PhD thesis
http://www.lulu.com/content/2043514

http://cs.anu.edu.au/courses/COMP8620/