  A central task of artificial intelligence is the design of artificial agents
that act towards specified goals in partially observed environments. Since such
environments frequently include interaction over time with other agents with
their own goals, reasoning about such interaction relies on sequential
game-theoretic models such as extensive-form games or some of their succinct
representations such as multi-agent influence diagrams. The current algorithms
for calculating equilibria either work with inefficient representations,
possibly doubly exponential inthe number of time steps, or place strong
assumptions on the game structure. In this paper,we propose a sampling-based
approach, which calculates extensive-form correlated equilibria with small
representations without placing such strong assumptions. Thus, it is practical
in situations where the previous approaches would fail. In addition, our
algorithm allows control over characteristics of the target equilibrium, e.g.,
we can ask for an equilibrium with high social welfare. Our approach is based
on a multiplicativeweight update algorithm analogous to AdaBoost, and Markov
chain Monte Carlo sampling. We prove convergence guarantees and explore the
utility of our approach on several moderately sized multi-player games.
