Socially intelligent reasoning for autonomous agents

Socially intelligent agents are autonomous problem solvers that have to achieve their objectives by interacting with other similarly autonomous entities. A major concern, therefore, is with the design of the decision-making mechanism that such agents employ in order to determine which actions to take to achieve their goals. We propose a framework for making socially acceptable decisions, based on social welfare functions, that combines social and individual perspectives in a unified and flexible manner. The framework is realized in an exemplar computational setting and an empirical analysis is made of the relative performance of varying sociable decision-making functions in a range of environments. This analysis is then used to design an agent that adapts its decision-making to reflect the resource constraints that it faces at any given time. A further round of empirical evaluation shows how adding such a meta-level mechanism enhances the performance of the agent by directing reasoning to adopt different strategies in different contexts. Finally, the possibility and efficacy of making the metalevel mechanism adaptive, so that experience of past encounters can be factored into the decision-making, is demonstrated.