Abstract:

To create new and better agents in multi-agent environments, we may want to examine the strategies of several existing agents, in order to combine their best skills. One problem is that in general, we won’t know what those strategies are; instead, we’ll only have observations of the agents’ interactions with other agents. In this talk, I describe how to take a set of interaction traces produced by different pairs of players in a two-player repeated game, and then find the best way to combine them into a composite strategy. I also describe how to incorporate the composite strategy into an existing agent, as an enhancement of the agent’s original strategy. In cross-validated experiments involving 126 agents (most of which written by students as class projects) for the Iterated Prisoner’s Dilemma, Iterated Chicken Game, and Iterated Battle of the Sexes, composite strategies produced from these agents were able to make improvement to the performance of nearly all of the agents.

The speaker will also talk about a technique, Symbolic Noise Detection (SND), for detecting noise (i.e., mistakes or miscommunications) among agents in repeated games. The idea behind SND is that if we can build a model of the other agent's behavior, we can use this model to detect and correct actions that have been affected by noise. In the 20th Anniversary Iterated Prisoner's Dilemma competition, the SND agent placed third in the “noise” category, and was the best performer among programs that had no “slave” programs feeding points to them. I'll discuss how to combine SND with the strategy synthesis technique in order to produce agents that perform well in noisy, cooperative environments.