{"type":"ScholarlyArticle","title":"Learning against opponents with bounded memory","authors":[{"name":"Rob Powers","slug":"rob-powers","given":"Rob","family":"Powers","affiliation":""},{"name":"Yoav Shoham","slug":"yoav-shoham","given":"Yoav","family":"Shoham","affiliation":""}],"abstract":"Recently, a number of authors have proposed criteria for evaluating learning algorithms in multiagent systems. While well-justified, each of these has generally given little attention to one of the main challenges of a multi-agent setting: the capability of the other agents to adapt and learn as well. We propose extending existing criteria to apply to a class of adaptive opponents with bounded memory. We then show an algorithm that provably achieves an ǫ-best response against this richer class of opponents while simultaneously guaranteeing a minimum payoff against any opponent and performing well in self-play. This new algorithm also demonstrates strong performance in empirical tests against a variety of opponents in a wide range of environments.","doi":null,"url":"https://ijcai.org/proceedings/2005/167","pdf":"https://ijcai.org/proceedings/2005/0167.pdf","markdown":"https://ijcai.org/proceedings/2005/167.md","bibtex":"https://ijcai.org/proceedings/2005/bibtex/167","volume":{"slug":"2005","title":"Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2005","url":"https://ijcai.org/proceedings/2005/","year":2005,"location":"Edinburgh, Scotland, UK","dates":"","editors":"","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":167,"pages":{"first":817,"last":821},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}