  We consider the problem of how to design large decentralized multi-agent
systems (MAS's) in an automated fashion, with little or no hand-tuning. Our
approach has each agent run a reinforcement learning algorithm. This converts
the problem into one of how to automatically set/update the reward functions
for each of the agents so that the global goal is achieved. In particular we do
not want the agents to ``work at cross-purposes'' as far as the global goal is
concerned. We use the term artificial COllective INtelligence (COIN) to refer
to systems that embody solutions to this problem. In this paper we present a
summary of a mathematical framework for COINs. We then investigate the
real-world applicability of the core concepts of that framework via two
computer experiments: we show that our COINs perform near optimally in a
difficult variant of Arthur's bar problem (and in particular avoid the tragedy
of the commons for that problem), and we also illustrate optimal performance
for our COINs in the leader-follower problem.
