On the emergence of social conventions: modeling, analysis, and simulations
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Graphical Models for Game Theory
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Computing pure nash equilibria in graphical games via markov random fields
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
What evolutionary game theory tells us about multiagent learning
Artificial Intelligence
State-coupled replicator dynamics
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Journal of Artificial Intelligence Research
Multi-agent influence diagrams for representing and solving games
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Learning graphical game models
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Learning and predicting dynamic networked behavior with graphical multiagent models
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Exploiting model equivalences for solving interactive dynamic influence diagrams
Journal of Artificial Intelligence Research
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A dynamic model of a multiagent system defines a probability distribution over possible system behaviors over time. Alternative representations for such models present tradeoffs in expressive power, and accuracy and cost for inferential tasks of interest. In a history-dependent representation, behavior at a given time is specified as a probabilistic function of some portion of system history. Models may be further distinguished based on whether they specify individual or joint behavior. Joint behavior models are more expressive, but in general grow exponentially in number of agents. Graphical multiagent models (GMMs) provide a more compact representation of joint behavior, when agent interactions exhibit some local structure. We extend GMMs to condition on history, thus supporting inference about system dynamics. To evaluate this hGMM representation we study a voting consensus scenario, where agents on a network attempt to reach a preferred unanimous vote through a process of smooth fictitious play. We induce hGMMs and individual behavior models from example traces, showing that the former provide better predictions, given limited history information. These hGMMs also provide advantages for answering general inference queries compared to sampling the true generative model.