Proceedings of the Annual Meeting of the Cognitive Science Society

Any designer of intelligent agents in a multiagent system is faced with the choice of encoding a strat?egy of interaction with other agents. If the nature of other agents are known in advance, a suitable strategy may be chosen from the continuum be?tween completely selfish behavior on one extreme and a philanthropic behavior on the other. In an open and dynamic system, however, it is unrealis?tic to assume that the nature of all other agents, possibly designed and used by users with very dif?ferent goals and motivations, are known precisely. In the presence of this uncertainty, is it possible to build agents that adapt their behavior to interact appropriately with the particular group of agents in the current scenario? W e address this question by borrowing on the simple yet powerful concept of re?ciprocal behavior. W e propose a stochastic decision making scheme which promotes reciprocity among agents. Using a package delivery problem we show that reciprocal behavior can lead to system-wide co?operation, and hence close to optimal global perfor?mance can be achieved even though each individued agent chooses actions to benefit itself. More inter?estingly, we show that agents who do not help others perform worse in the long run when compared with reciprocal agents. Thus it is to the best interest of every individual agent to help other agents.