Graphical Models for Game Theory
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Run the GAMUT: A Comprehensive Approach to Evaluating Game-Theoretic Algorithms
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Learning payoff functions in infinite games
Machine Learning
Learning graphical model structure using L1-regularization paths
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
History-dependent graphical multiagent models
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Graph formation effects on social welfare and inequality in a networked resource game
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Learning equilibria of games via payoff queries
Proceedings of the fourteenth ACM conference on Electronic commerce
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Graphical games provide compact representation of a multiagent interaction when agents' payoffs depend only on actions of agents in their local neighborhood. We formally describe the problem of learning a graphical game model from limited observation of the payoff function, define three performance metrics for evaluating learned games, and investigate several learning algorithms based on minimizing empirical loss. Our first algorithm is a branch-and-bound search, which takes advantage of the structure of the empirical loss function to derive upper and lower bounds on loss at every node of the search tree. We also examine a greedy heuristic and local search algorithms. Our experiments with directed graphical games show that (i) when only a small sample of profile payoffs is available, branch-and-bound significantly outperforms other methods, and has competitive running time, but (ii) when many profiles are observed, greedy is nearly optimal and considerably better than other methods, at a fraction of branch-and-bound's running time. The results are comparable for undirected graphical games and when payoffs are sampled with noise.