Advanced Artificial Intelligence: 15-780 / 16-731, Spring 2005

Class meeting times: Tue/Thu 1:30 - 2:50, Newell-Simon Hall 1305


Mike Lewicki
Mellon Institute 115K, x8-3921


Tuomas Sandholm
Wean 7127, x8-8216

Office hour: Tu 3-3:30.

Teaching Assistants

Andrew Gilpin
Wean 3715 x8-1405


Jean Oh
NSH 1517, x8-5921Course book

Russell and Norvig: Artificial Intelligence: A Modern Approach, second edition, Prentice Hall.


            Evaluation will be based on written and programming homeworks/projects.  There will be no exams.

Course schedule (will be updated dynamically)

Segment I: Search (Tuomas).

Topics: AI as the design of agents, linear programming refresher, uninformed search, constraint satisfaction problems and algorithms, informed search, heuristics, upper and lower bounding techniques, mixed integer programming, iterative refinement search.

Segment II: Probabilistic Inference and Learning (Mike)


Topics: AI as optimal decision making, probability theory, probabilistic reasoning, Bayesian networks, learning and inference algorithms, stochastic methods, sequential reasoning, HMMs, MDPs, POMDPs, efficient reasoning.

Segment III: Computational Game Theory (Tuomas)


Topics: Game types, game representations, solution concepts, algorithms for solving games, algorithms that play well albeit not optimally, mechanism design.

Segment IV: Natural Intelligence (Mike)


Topics: Properties of natural intelligent systems, natural perception, how the brains does it, open issues in AI.