Suppose you are interacting with one or more agents who are unfamiliar to you. How can you decide the best way to behave? Suppose further that there is “noise” in the environment, i.e., your observations of their behavior (and their observations of your behavior) are not always correct. How should this affect your behavior?

Professor Nau will discuss the above questions in the context of several well-known nonzero-sum games, including the Iterated Prisoner`s Dilemma, Iterated Chicken Game, and Iterated Battle of the Sexes. He will describe algorithms that build predictive models of agents based on observations of their behavior. These models can be used to filter out noise, and to construct strategies that optimize expected utility.

Experimental evaluations of the above algorithms show that they work quite well. For example, DBS, an agent based on one of the algorithms, placed third out of 165 contestants in an international competition of the Iterated Prisoner`s Dilemma with Noise. Only two agents scored higher than DBS, and both of them used a “master-and-slaves” strategy in which a large number of “slave” agents deliberately conspired to raise the score of a single “master” agent.