Forming stable partnerships

Autonomous agents interacting in an open world can be considered to be primarily driven by self interests. In this paper, we evaluate the hypotheses that self-interested agents with complementary expertise can learn to recognize cooperation possibilities and develop stable, mutually beneficial partnerships that is resistant to exploitation by malevolent agents. Previous work in this area has prescribed a strategy of reciprocal behavior for promoting and sustaining cooperation among such cognitive learning agents. We develop on that work by expanding the task cost metric to include both time of completion and quality of performance. Different task types are assumed and, in contrast to previous work, we use heterogeneous agents with varying expertise for different task types. This necessitates the incorporation of cognitive abilities, including an understanding of one’s own capabilities and learning about other’s capabilities within the reciprocity framework.