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Efficient collaborative decision making is an important challenge formultiagent systems. Finding optimal joint actions is especially challengingwhen each agent has only imperfect information about the state of itsenvironment. Such problems can be modeled as collaborative Bayesian games inwhich each agent receives private information in the form of its type. However,representing and solving such games requires space and computation timeexponential in the number of agents. This article introduces collaborativegraphical Bayesian games (CGBGs), which facilitate more efficient collaborativedecision making by decomposing the global payoff function as the sum of localpayoff functions that depend on only a few agents. We propose a framework forthe efficient solution of CGBGs based on the insight that they posses twodifferent types of independence, which we call agent independence and typeindependence. In particular, we present a factor graph representation thatcaptures both forms of independence and thus enables efficient solutions. Inaddition, we show how this representation can provide leverage in sequentialtasks by using it to construct a novel method for decentralized partiallyobservable Markov decision processes. Experimental results in both random andbenchmark tasks demonstrate the improved scalability of our methods compared toseveral existing alternatives.

Exploiting Agent and Type Independence in Collaborative Graphical Bayesian Games