Temporal Action-Graph Games: A New Representation for Dynamic Games

Jiang, Albert Xin, Leyton-Brown, Kevin, Pfeffer, Avi

arXiv.org Artificial Intelligence 

In this paper we introduce temporal action graph games (TAGGs), a novel graphical representation of imperfect-information extensive form games. We show that when a game involves anonymity or context-specific utility independencies, its encoding as a TAGG can be much more compact than its direct encoding as a multiagent influence diagram (MAID).We also show that TAGGs can be understood as indirect MAID encodings in which many deterministic chance nodes are introduced. We provide an algorithm for computing with TAGGs, and show both theoretically and empirically that our approach improves significantly on the previous state of the art.

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