@inproceedings{2453d4db46f84f4baf4fa035c692f19e,
title = "Learning to identify winning coalitions in the PAC model",
abstract = "We consider PAG learning of simple cooperative games, in which the coalitions are partitioned into {"}winning{"} and {"}losing{"} coalitions. We analyze the complexity of learning a suitable concept class via its Vapnik-Chervonenkis (VC) dimension, and provide an algorithm that learns this class. Furthermore, we study constrained simple games; we demonstrate that the VC dimension can be dramatically reduced when one allows only a single minimum winning coalition (even more so when this coalition has cardinality 1), whereas other interesting constraints do not significantly lower the dimension.",
keywords = "Coalition formation, PAC learning",
author = "Procaccia, {Ariel D.} and Rosenschein, {Jeffrey S.}",
year = "2006",
doi = "10.1145/1160633.1160751",
language = "American English",
isbn = "1595933034",
series = "Proceedings of the International Conference on Autonomous Agents",
pages = "673--675",
booktitle = "Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems",
note = "Fifth International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS ; Conference date: 08-05-2006 Through 12-05-2006",
}