{"type":"ScholarlyArticle","title":"Real-Time Solving of Quantified CSPs Based on Monte-Carlo Game Tree Search","authors":[{"name":"Satomi Baba","slug":"satomi-baba","given":"Satomi","family":"Baba","affiliation":""},{"name":"Yongjoon Joe","slug":"yongjoon-joe","given":"Yongjoon","family":"Joe","affiliation":""},{"name":"Atsushi Iwasaki","slug":"atsushi-iwasaki","given":"Atsushi","family":"Iwasaki","affiliation":""},{"name":"Makoto Yokoo","slug":"makoto-yokoo","given":"Makoto","family":"Yokoo","affiliation":""}],"abstract":"We develop a real-time algorithm based on a Monte-Carlo game tree search for solving a quantified constraint satisfaction problem (QCSP), which is a CSP where some variables are universally quantified. A universally quantified variable represents a choice of nature or an adversary. The goal of a QCSP is to make a robust plan against an adversary. However, obtaining a complete plan off-line is intractable when the size of the problem becomes large. Thus, we need to develop a real-time algorithm that sequentially selects a promising value at each deadline. Such a problem has been considered in the field of game tree search. In a standard game tree search algorithm, developing a good static evaluation function is crucial. However, developing a good static evaluation function for a QCSP is very difficult since it must estimate the possibility that a partially assigned QCSP is solvable. Thus, we apply a Monte-Carlo game tree search technique called UCT. However, the simple application of the UCT algorithm does not work since the player and the adversary are asymmetric, i.e., finding a game sequence where the player wins is very rare. We overcome this difficulty by introducing constraint propagation techniques. We experimentally compare the winning probability of our UCT-based algorithm and the state-of-the-art alpha-beta search algorithm. Our results show that our algorithm outperforms the state-of-the-art algorithm in large-scale problems.","doi":"10.5591/978-1-57735-516-8/IJCAI11-116","url":"https://ijcai.org/proceedings/2011/116","pdf":"https://ijcai.org/proceedings/2011/0116.pdf","markdown":"https://ijcai.org/proceedings/2011/116.md","bibtex":"https://ijcai.org/proceedings/2011/bibtex/116","volume":{"slug":"2011","title":"Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2011","url":"https://ijcai.org/proceedings/2011/","year":2011,"location":"Barcelona, Catalonia, Spain","dates":"16–22 July 2011","editors":"Toby Walsh","isbn":"978-1-57735-512-0; 978-1-57735-513-7; 978-1-57735-514-4; 978-1-57735-515-1; 978-1-57735-516-8","publisher":"AAAI Press/International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":116,"pages":{"first":655,"last":660},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}