HTN Plan Repair via Model Transformation
dc.contributor.author | Holler, Daniel | |
dc.contributor.author | Bercher, Pascal | |
dc.contributor.author | Behnke, Gregor | |
dc.contributor.author | Biundo, Susanne | |
dc.contributor.editor | Schmid, Ute | |
dc.contributor.editor | Klügl, Franziska | |
dc.contributor.editor | Wolter, Diedrich | |
dc.coverage.spatial | Bamberg, Germany | |
dc.date.accessioned | 2024-01-24T22:59:31Z | |
dc.date.created | September 21–25, 2020 | |
dc.date.issued | 2020 | |
dc.date.updated | 2022-10-02T07:17:37Z | |
dc.description.abstract | To make planning feasible, planning models abstract from many details of the modeled system. When executing plans in the actual system, the model might be inaccurate in a critical point, and plan execution may fail. There are two options to handle this case: the previous solution can be modified to address the failure (plan repair), or the planning process can be re-started from the new situation (re-planning). In HTN planning, discarding the plan and generating a new one from the novel situation is not easily possible, because the HTN solution criteria make it necessary to take already executed actions into account. Therefore all approaches to repair plans in the literature are based on specialized algorithms. In this paper, we discuss the problem in detail and introduce a novel approach that makes it possible to use unchanged, off-the-shelf HTN planning systems to repair broken HTN plans. That way, no specialized solvers are needed. | en_AU |
dc.description.sponsorship | Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) – Projektnummer 232722074 – SFB 1102 / Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 232722074 – SFB 1102. | en_AU |
dc.format.mimetype | application/pdf | en_AU |
dc.identifier.isbn | 978-3-030-58284-5 | en_AU |
dc.identifier.uri | http://hdl.handle.net/1885/311833 | |
dc.language.iso | en_AU | en_AU |
dc.publisher | Springer Nature Switzerland AG | en_AU |
dc.relation.ispartofseries | 43rd German Conference on Artificial Intelligence (Künstliche Intelligenz) | en_AU |
dc.rights | © Springer Nature Switzerland AG 2020 | en_AU |
dc.subject | HTN Planning | en_AU |
dc.subject | Plan repair | en_AU |
dc.subject | Re-planning | en_AU |
dc.title | HTN Plan Repair via Model Transformation | en_AU |
dc.type | Conference paper | en_AU |
local.bibliographicCitation.lastpage | 101 | en_AU |
local.bibliographicCitation.startpage | 88 | en_AU |
local.contributor.affiliation | Holler, Daniel, Saarland University | en_AU |
local.contributor.affiliation | Bercher, Pascal, College of Engineering and Computer Science, ANU | en_AU |
local.contributor.affiliation | Behnke, Gregor , University of Freiburg | en_AU |
local.contributor.affiliation | Biundo, Susanne, Ulm University | en_AU |
local.contributor.authoremail | u1092535@anu.edu.au | en_AU |
local.contributor.authoruid | Bercher, Pascal, u1092535 | en_AU |
local.description.embargo | 2099-12-31 | |
local.description.notes | Imported from ARIES | en_AU |
local.description.refereed | Yes | |
local.identifier.absfor | 460209 - Planning and decision making | en_AU |
local.identifier.ariespublication | a383154xPUB16986 | en_AU |
local.identifier.doi | 10.1007/978-3-030-58285-2_7 | en_AU |
local.identifier.scopusID | 2-s2.0-85091145262 | |
local.identifier.uidSubmittedBy | a383154 | en_AU |
local.publisher.url | https://link.springer.com/ | en_AU |
local.type.status | Published Version | en_AU |
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