{"type":"ScholarlyArticle","title":"Heuristic Planning for PDDL+ Domains","authors":[{"name":"Wiktor Mateusz Piotrowski","slug":"wiktor-mateusz-piotrowski","given":"Wiktor Mateusz","family":"Piotrowski","affiliation":""},{"name":"Maria Fox","slug":"maria-fox","given":"Maria","family":"Fox","affiliation":""},{"name":"Derek Long","slug":"derek-long","given":"Derek","family":"Long","affiliation":""},{"name":"Daniele Magazzeni","slug":"daniele-magazzeni","given":"Daniele","family":"Magazzeni","affiliation":""},{"name":"Fabio Mercorio","slug":"fabio-mercorio","given":"Fabio","family":"Mercorio","affiliation":""}],"abstract":"Planning with hybrid domains modelled in PDDL+ has been gaining research interest in the Automated Planning community in recent years. Hybrid domain models capture a more accurate representation of real world problems that involve continuous processes than is possible using discrete systems. However, solving problems represented as PDDL+ domains is very challenging due to the construction of complex system dynamics, including non-linear processes and events. In this paper we introduce DiNo, a new planner capable of tackling complex problems with non-linear system dynamcs governing the continuous evolution of states. DiNo is based on the discretise-and-validate approach and uses the novel Staged Relaxed Planning Graph+ (SRPG+) heuristic, which is introduced in this paper. Although several planners have been developed to work with subsets of PDDL+ features, or restricted forms of processes, DiNo is currently the only heuristic planner capable of handling non-linear system dynamics combined with the full PDDL+ feature set.","doi":null,"url":"https://ijcai.org/proceedings/2016/455","pdf":"https://ijcai.org/proceedings/2016/0455.pdf","markdown":"https://ijcai.org/proceedings/2016/455.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/455","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":455,"pages":{"first":3213,"last":3218},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}