Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/36282
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dc.contributor.authorCatalano, Giancarlo A P Ien_UK
dc.contributor.authorBrownlee, Alexanderen_UK
dc.contributor.authorCairns, Daviden_UK
dc.contributor.authorMcCall, Johnen_UK
dc.contributor.authorFyvie, Martinen_UK
dc.contributor.authorAinslie, Russellen_UK
dc.date.accessioned2024-10-08T00:01:03Z-
dc.date.available2024-10-08T00:01:03Z-
dc.identifier.urihttp://hdl.handle.net/1893/36282-
dc.description.abstractThere are many critical optimisation tasks that metaheuris-tic approaches have been shown to be able to solve effectively. Despite promising results, users might not trust these algorithms due to their intrinsic lack of interpretability. This paper demonstrates the use of ex-plainability to resolve this issue by producing human-interpretable insights that focus on simplicity, fitness and linkage. Our explainability approach revolves around the concept of Partial Solutions , which assist in breaking up the solutions of optimisation problems into smaller components. We first expand upon our previous research proposing the technique, and then provide a use case on the Staff Ros-tering task: a large and otherwise uninterpretable optimisation problem with ethical implications due to its direct impact on humans. The explanations consist in rota assignments for interacting groups of workers, along with the reasons why they are interacting. Lastly, some experiments are used to ascertain that the algorithms work as intended and for hyperparameter tuning. The results suggest that our methodology is capable of presenting in-sightful information for the Staff Rostering problem, by producing both local explanations of solutions and global explanations of the problem definition.en_UK
dc.language.isoenen_UK
dc.publisherSpringeren_UK
dc.relationCatalano GAPI, Brownlee A, Cairns D, McCall J, Fyvie M & Ainslie R (2024) Explaining a Staff Rostering Problem using Partial Solutions. In: <i>TBC</i>. Lecture Notes in Artificial Intelligence. AI-2024 Forty-fourth SGAI International Conference on Artificial Intelligence, Cambridge, 17.12.2024-19.12.2024. Cham, Switzerland: Springer.en_UK
dc.relation.ispartofseriesLecture Notes in Artificial Intelligenceen_UK
dc.rightsThis item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.en_UK
dc.subjectExplainabilityen_UK
dc.subjectXAIen_UK
dc.subjectJob Schedulingen_UK
dc.subjectMetaheuristicsen_UK
dc.titleExplaining a Staff Rostering Problem using Partial Solutionsen_UK
dc.typeConference Paperen_UK
dc.rights.embargodate2027-08-30en_UK
dc.rights.embargoreason[BT_Paper__correct_format_.pdf] Publisher requires embargo of 12 months after publication.en_UK
dc.citation.issn2945-9133en_UK
dc.type.statusAM - Accepted Manuscripten_UK
dc.author.emailalexander.brownlee@stir.ac.uken_UK
dc.citation.btitleTBCen_UK
dc.citation.conferencedates2024-12-17 - 2024-12-19en_UK
dc.citation.conferencelocationCambridgeen_UK
dc.citation.conferencenameAI-2024 Forty-fourth SGAI International Conference on Artificial Intelligenceen_UK
dc.publisher.addressCham, Switzerlanden_UK
dc.description.notesOutput Status: Forthcomingen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationRobert Gordon Universityen_UK
dc.contributor.affiliationRobert Gordon Universityen_UK
dc.contributor.affiliationBT Group Plcen_UK
dc.identifier.wtid2042749en_UK
dc.contributor.orcid0000-0003-2892-5059en_UK
dc.contributor.orcid0000-0002-0246-3821en_UK
dc.date.accepted2024-08-30en_UK
dcterms.dateAccepted2024-08-30en_UK
dc.date.filedepositdate2024-09-03en_UK
rioxxterms.apcnot requireden_UK
rioxxterms.typeConference Paper/Proceeding/Abstracten_UK
rioxxterms.versionAMen_UK
local.rioxx.authorCatalano, Giancarlo A P I|en_UK
local.rioxx.authorBrownlee, Alexander|0000-0003-2892-5059en_UK
local.rioxx.authorCairns, David|0000-0002-0246-3821en_UK
local.rioxx.authorMcCall, John|en_UK
local.rioxx.authorFyvie, Martin|en_UK
local.rioxx.authorAinslie, Russell|en_UK
local.rioxx.projectInternal Project|University of Stirling|https://isni.org/isni/0000000122484331en_UK
local.rioxx.freetoreaddate2027-08-30en_UK
local.rioxx.licencehttp://www.rioxx.net/licenses/under-embargo-all-rights-reserved||2027-08-29en_UK
local.rioxx.licencehttp://www.rioxx.net/licenses/all-rights-reserved|2027-08-30|en_UK
local.rioxx.filenameBT_Paper__correct_format_.pdfen_UK
local.rioxx.filecount1en_UK
local.rioxx.source2945-9133en_UK
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings

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