{"type":"ScholarlyArticle","title":"Monte-Carlo Exploration for Deterministic Planning","authors":[{"name":"Hootan Nakhost","slug":"hootan-nakhost","given":"Hootan","family":"Nakhost","affiliation":""},{"name":"Martin Müller","slug":"martin-muller","given":"Martin","family":"Müller","affiliation":""}],"abstract":"Search methods based on Monte-Carlo simulation have recently led to breakthrough performance improvements in difficult game-playing domains such as Go and General Game Playing. Monte-Carlo Random Walk (MRW) planning applies Monte-Carlo ideas to deterministic classical planning. In the forward chaining planner Arvand, Monte-Carlo random walks are used to explore the local neighborhood of a search state for action selection. In contrast to the stochastic local search approach used in the recent planner Identidem, random walks yield a larger and unbiased sample of the search neighborhood, and require state evaluations only at the endpoints of each walk. On IPC-4 competition problems, the performance of Arvand is competitive with state of the art systems.Hootan Nakhost, Martin Müller","doi":null,"url":"https://ijcai.org/proceedings/2009/294","pdf":"https://ijcai.org/proceedings/2009/0294.pdf","markdown":"https://ijcai.org/proceedings/2009/294.md","bibtex":"https://ijcai.org/proceedings/2009/bibtex/294","volume":{"slug":"2009","title":"Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2009","url":"https://ijcai.org/proceedings/2009/","year":2009,"location":"Pasadena, California, USA","dates":"","editors":"Hiroaki Kitano","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":294,"pages":{"first":1766,"last":1770},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}