We introduce Game networks (G nets), a novel representation for multi-agent decision problems. Compared to other game-theoretic representations, such as strategic or extensive forms, G nets are more structured and more compact; more fundamentally, G nets consti tute a computationally advantageous frame work for strategic inference, as both prob ability and utility independencies are cap tured in the structure of the network and can be exploited in order to simplify the infer ence process. An important aspect of multi agent reasoning is the identification of some or all of the strategic equilibria in a game; we present original convergence methods for strategic equilibrium which can take advan tage of strategic separabilities in the G net structure in order to simplify the computa tions. Specifically, we describe a method which identifies a unique equilibrium as a function of the game payoffs, and one which identifies all equilibria.
La REF proposes a continuation method for finding one or all equilibria in a G net, a representation that is very similar to MAIDs.
Game Networks
ArXiv
ArXiv
Computer Science