  Computing Nash equilibria for strategic multi-agent systems is challenging
for expensive black box systems. Motivated by the ubiquity of games involving
exploitation of common resources, this paper considers the above problem for
potential games. We use the Bayesian optimization framework to obtain novel
algorithms to solve finite (discrete action spaces) and infinite (real interval
action spaces) potential games, utilizing the structure of potential games.
Numerical results illustrate the efficiency of the approach in computing the
Nash equilibria of static potential games and linear Nash equilibria of dynamic
potential games.
