Proceedings of the AAAI Conference on Artificial Intelligence, 21
Action-Graph Games (AGGs) are a fully expressive game representation which can compactly express strict and context-specific independence and anonymity structure in players' utility functions. We present an efficient algorithm for computing expected payoffs under mixed strategy profiles. This algorithm runs in time polynomial in the size of the AGG representation (which is itself polynomial in the number of players when the in-degree of the action graph is bounded). We also present an extension to the AGG representation which allows us to compactly represent a wider variety of structured utility functions.