{"type":"ScholarlyArticle","title":"Generalized Rapid Action Value Estimation","authors":[{"name":"Tristan Cazenave","slug":"tristan-cazenave","given":"Tristan","family":"Cazenave","affiliation":""}],"abstract":"Monte Carlo Tree Search (MCTS) is the state of the art algorithm for many games including the game of Go and General Game Playing (GGP). The standard algorithm for MCTS is Upper Confidence bounds applied to Trees (UCT). For games such as Go a big improvement over UCT is the Rapid Action Value Estimation (RAVE) heuristic. We propose to generalize the RAVE heuristic so as to have more accurate estimates near the leaves. We test the resulting algorithm named GRAVE for Atarigo, Knighthrough, Domineering and Go.","doi":null,"url":"https://ijcai.org/proceedings/2015/112","pdf":"https://ijcai.org/proceedings/2015/0112.pdf","markdown":"https://ijcai.org/proceedings/2015/112.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/112","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":"Heuristic Search","track":"Main Track","number":112,"pages":{"first":754,"last":759},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}