---
title: "Learning Cooperative Games"
authors:
  - "Maria-Florina Balcan"
  - "Ariel D. Procaccia"
  - "Yair Zick"
doi: ""
url: "https://ijcai.org/proceedings/2015/73"
pdf: "https://ijcai.org/proceedings/2015/0073.pdf"
venue: "Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence"
year: 2015
pages: "475-480"
track: "Main Track"
keywords:
---

# Learning Cooperative Games

Maria-Florina Balcan, Ariel D. Procaccia, Yair Zick

*Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence*, 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.