Balcan, Maria-Florina Procaccia, Ariel D. Zick, Yair Learning Cooperative Games <p>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</p> Machine Learning 2015-01-01
    https://kilthub.cmu.edu/articles/journal_contribution/Learning_Cooperative_Games/6475850
10.1184/R1/6475850.v1