{"type":"ScholarlyArticle","title":"Efficient Search with an Ensemble of Heuristics","authors":[{"name":"Mike Phillips","slug":"mike-phillips","given":"Mike","family":"Phillips","affiliation":""},{"name":"Venkatraman Narayanan","slug":"venkatraman-narayanan","given":"Venkatraman","family":"Narayanan","affiliation":""},{"name":"Sandip Aine","slug":"sandip-aine","given":"Sandip","family":"Aine","affiliation":""},{"name":"Maxim Likhachev","slug":"maxim-likhachev","given":"Maxim","family":"Likhachev","affiliation":""}],"abstract":"Recently, a number of papers have shown that for many domains, using multiple heuristics in independent searches performs better than combining them into a single heuristic. Furthermore, using a large number of “weak” heuristics could potentially eliminate the need for the careful design of a few. The standard approach to distribute computation in these multi-heuristic searches is to rotate through the heuristics in a round-robin fashion. However, this strategy can be inefficient especially in the case when only a few of the heuristics are leading to progress. In this paper, we present two principled methods to adaptively distribute computation time among the different searches of the Multi- Heuristic A* algorithm. The first method, Meta-A*, constructs and searches a meta-graph, which represents the problem of finding the best heuristic as the problem of minimizing the total number of expansions. The second treats the scheduling of searches with different heuristics as a multi-armed bandit problem. It applies Dynamic Thompson Sampling (DTS) to keep track of what searches are making progress the most and continuously re-computes the schedule of searches based on this information. We provide a theoretical analysis and compare our new strategies with the round-robin method on a 12-DOF full-body motion planning problem and on sliding tile puzzle problems. In these experiments, we used up to 20 heuristics and observed a several times speedup without loss in solution quality.","doi":null,"url":"https://ijcai.org/proceedings/2015/116","pdf":"https://ijcai.org/proceedings/2015/0116.pdf","markdown":"https://ijcai.org/proceedings/2015/116.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/116","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":116,"pages":{"first":784,"last":790},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}