Game Networks
This work addresses the problem of efficient strategic reasoning in multi-agent systems for researchers and practitioners in game theory and AI, though it appears incremental as it builds on existing game-theoretic representations with new computational methods.
The authors tackled the representation of multi-agent decision problems by introducing Game networks (G nets), which are more structured and compact than existing forms, and developed convergence methods that exploit strategic separabilities to simplify identifying strategic equilibria, resulting in computational advantages for strategic inference.
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 constitute a computationally advantageous framework for strategic inference, as both probability and utility independencies are captured in the structure of the network and can be exploited in order to simplify the inference 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 advantage of strategic separabilities in the G net structure in order to simplify the computations. Specifically, we describe a method which identifies a unique equilibrium as a function of the game payoffs, and one which identifies all equilibria.