Learning Cooperative Games
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 475-480.
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.
@inproceedings{ijcai2015p73,
title = {Learning Cooperative Games},
author = {Balcan, Maria-Florina and Procaccia, Ariel D. and Zick, Yair},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {475--480},
year = {2015},
month = {7},
note = {Main Track},
url = {https://ijcai.org/proceedings/2015/73},
}
TY - CPAPER TI - Learning Cooperative Games AU - Balcan, Maria-Florina AU - Procaccia, Ariel D. AU - Zick, Yair T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 475 EP - 480 UR - https://ijcai.org/proceedings/2015/73 ER -Copied
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