To help or not to help

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 sel sh 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 We address this question by borrowing on the simple yet powerful concept of re ciprocal behavior We 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 individual agent chooses actions to bene t 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