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arXiv · 1505.00039

Learning Cooperative Games

Maria-Florina Balcan·Ariel D. Procaccia·Yair Zick
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Abstract

This paper explores a PAC (probably approximately correct) learning model in cooperative games. Specifically, we are given $m$ random samples of coalitions and their values, taken from some unknown cooperative game; can we predict the values of unseen coalitions? We study the PAC learnability of several well-known classes of cooperative games, such as network flow games, threshold task games, and induced subgraph games. We also establish a novel connection between PAC learnability and core stability: for games that are efficiently learnable, it is possible to find payoff divisions that are likely to be stable using a polynomial number of samples.

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Maria-Florina Balcan, Ariel D. Procaccia, Yair Zick. 2015-04-30. Learning Cooperative Games. https://arxiv.org/abs/1505.00039

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