{"type":"ScholarlyArticle","title":"Stochastic Planning in Large Search Spaces","authors":[{"name":"Bilal Kartal","slug":"bilal-kartal","given":"Bilal","family":"Kartal","affiliation":""}],"abstract":"Multi-agent planning approaches are employed for many problems including task allocation, surveillance and video games. In the first part of my thesis, we study two multi-robot planning problems, i.e. patrolling and task allocation. For the patrolling problem, we present a novel stochastic search technique, Monte Carlo Tree Search with Useful Cycles, that can generate optimal cyclic patrol policies with theoretical convergence guarantees. For the multi-robot task allocation problem, we propose an Monte Carlo Tree Search based satisficing method using branch and bound paradigm along with a novel search parallelization technique. In the second part of my thesis, we develop a stochastic multi-agent narrative planner employing MCTS along with new heuristic and pruning methods applicable for other planning domains as well.","doi":null,"url":"https://ijcai.org/proceedings/2016/574","pdf":"https://ijcai.org/proceedings/2016/0574.pdf","markdown":"https://ijcai.org/proceedings/2016/574.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/574","volume":{"slug":"2016","title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2016","url":"https://ijcai.org/proceedings/2016/","year":2016,"location":"New York, NY, USA","dates":"9–15 July 2016","editors":"Subbarao Kambhampati","isbn":"978-1-57735-770-4; 978-1-57735-771-1","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":574,"pages":{"first":4000,"last":4000},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}