Date of Original Version



Conference Proceeding

Rights Management

Copyright © 2015 International Joint Conferences on Artificial Intelligence

Abstract or Description

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.



Published In

Proceedings of the International Joint Conference on Artificial Intelligence, 2015, 475-481.