  Multiagent systems deployed in the real world need to cooperate with other
agents (including humans) nearly as effectively as these agents cooperate with
one another. To design such AI, and provide guarantees of its effectiveness, we
need to clearly specify what types of agents our AI must be able to cooperate
with. In this work we propose a generic model of socially intelligent agents,
which are individually rational learners that are also able to cooperate with
one another (in the sense that their joint behavior is Pareto efficient). We
define rationality in terms of the regret incurred by each agent over its
lifetime, and show how we can construct socially intelligent agents for
different forms of regret. We then discuss the implications of this model for
the development of "robust" MAS that can cooperate with a wide variety of
socially intelligent agents.
