MASTERS @Tandy School of Computer Science, TU

In open environments there is no central control over agent behaviors. On the contrary, agents in such systems can be assumed to be primarily driven by self interests. In a number of situations, myopic decision making can lead to both unsatisfactory performance by individual agents and undesirable effects at the overall system level. Our goal is to understand domains and environments where self-interested agents may have incentives to cooperate. More specifically, we want to identify and design environmental characteristics and agent behavioral strategies such that the best self-interested behavior is to cooperate with other agents. In such environments, both individual and system-level performance is optimized when agents cooperate. To identify domains with these desirable characteristics, we have to establish mechanisms that can cooperate effectively and is resistant to exploitation. Under the assumption that agents remain in the system for significant time periods, or that the agent composition changes only slowly, we have been studying a probabilistic reciprocity-based strategy for promoting and sustaining cooperation in agent groups. This strategy improves both individual and group performance in the long run. We have also analyzed the effectiveness of this strategy when agents have incomplete information and can over or underestimate the amount of help received. While these initial studies help us understand some basic properties of reciprocity-based agent behaviors, we need to build the underlying theory and investigate other critical aspects of such strategies before practical multiagent systems can be built and deployed effectively in open environments.