  Efficient collaborative decision making is an important challenge for
multiagent systems. Finding optimal joint actions is especially challenging
when each agent has only imperfect information about the state of its
environment. Such problems can be modeled as collaborative Bayesian games in
which each agent receives private information in the form of its type. However,
representing and solving such games requires space and computation time
exponential in the number of agents. This article introduces collaborative
graphical Bayesian games (CGBGs), which facilitate more efficient collaborative
decision making by decomposing the global payoff function as the sum of local
payoff functions that depend on only a few agents. We propose a framework for
the efficient solution of CGBGs based on the insight that they posses two
different types of independence, which we call agent independence and type
independence. In particular, we present a factor graph representation that
captures both forms of independence and thus enables efficient solutions. In
addition, we show how this representation can provide leverage in sequential
tasks by using it to construct a novel method for decentralized partially
observable Markov decision processes. Experimental results in both random and
benchmark tasks demonstrate the improved scalability of our methods compared to
several existing alternatives.
