{"type":"ScholarlyArticle","title":"Learning Cooperative Games","authors":[{"name":"Maria-Florina Balcan","slug":"maria-florina-balcan","given":"Maria-Florina","family":"Balcan","affiliation":""},{"name":"Ariel D. Procaccia","slug":"ariel-d-procaccia","given":"Ariel D.","family":"Procaccia","affiliation":""},{"name":"Yair Zick","slug":"yair-zick","given":"Yair","family":"Zick","affiliation":""}],"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.","doi":null,"url":"https://ijcai.org/proceedings/2015/73","pdf":"https://ijcai.org/proceedings/2015/0073.pdf","markdown":"https://ijcai.org/proceedings/2015/73.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/73","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Game Theory","track":"Main Track","number":73,"pages":{"first":475,"last":480},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}