NONZERO
Non-Zero Sum Games
Suppose you are interacting with a collection of agents who are unfamiliar to you. How can you decide the best way to behave? How can you overcome difficulties that might arise from accidents or miscommunications?
We have investigated 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. We have developed 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.
Project lead: Dr. Dana Nau.
For additional information, please contact Dr. Dana Nau or Dr. Inon Zuckerman.
Last updated: November 2009 by John Dickerson.
Publication links contingent upon publication!
This page will be updated as our work enters print. For information about receiving draft publications, technical reports, and conference presentations, please do not hesitate to contact team members.
The following sections may include links to restricted access material. Please do not hesitate to contact a group member for instructions regarding how to obtain a username and password.
Presentation links contingent upon publication!
The following sections may include links to restricted access material. Please do not hesitate to contact a group member for instructions regarding how to obtain a username and password.
Software links contingent upon publication!