{"type":"ScholarlyArticle","title":"Refining Incomplete Planning Domain Models Through Plan Traces","authors":[{"name":"Hankz Hankui Zhuo","slug":"hankz-hankui-zhuo","given":"Hankz Hankui","family":"Zhuo","affiliation":""},{"name":"Tuan Anh Nguyen","slug":"tuan-anh-nguyen","given":"Tuan Anh","family":"Nguyen","affiliation":""},{"name":"Subbarao Kambhampati","slug":"subbarao-kambhampati","given":"Subbarao","family":"Kambhampati","affiliation":""}],"abstract":"Most existing work on learning planning models assumes that the entire model needs to be learned from scratch. A more realistic situation is that the planning agent has an incomplete model which it needs to refine through learning. In this paper we propose and evaluate a method for doing this. Our method takes as input an incomplete model (with missing preconditions and effects in the actions), as well as a set of plan traces that are known to be correct. It outputs a refined model that not only captures additional precondition/effect knowledge about the given actions, but also macro actions. We use a MAX-SAT framework for learning, where the constraints are derived from the executability of the given plan traces, as well as the preconditions/effects of the given incomplete model. Unlike traditional macro-action learners which use macros to increase the efficiency of planning (in the context of a complete model), our motivation for learning macros is to increase the accuracy (robustness) of the plans generated with the refined model. We demonstrate the effectiveness of our approach through a systematic empirical evaluation.","doi":null,"url":"https://ijcai.org/proceedings/2013/360","pdf":"https://ijcai.org/proceedings/2013/0360.pdf","markdown":"https://ijcai.org/proceedings/2013/360.md","bibtex":"https://ijcai.org/proceedings/2013/bibtex/360","volume":{"slug":"2013","title":"Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2013","url":"https://ijcai.org/proceedings/2013/","year":2013,"location":"Beijing, China","dates":"3–9 August 2013","editors":"Francesca Rossi","isbn":"978-1-57735-633-2","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":360,"pages":{"first":2451,"last":2457},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}