DC FieldValueLanguage
dc.contributor.authorKöcher, Aljosha-
dc.contributor.authorHeesch, Rene-
dc.contributor.authorWidulle, Niklas-
dc.contributor.authorNordhausen, Anna-
dc.contributor.authorPutzke, Julian-
dc.contributor.authorWindmann, Alexander-
dc.contributor.authorNiggemann, Oliver-
dc.date.accessioned2022-11-24T09:10:53Z-
dc.date.available2022-11-24T09:10:53Z-
dc.date.issued2021-12-31-
dc.identifier.isbn9781665497701-
dc.description.abstractManufacturing companies face challenges when it comes to quickly adapting their production control to fluctuating demands or changing requirements. Control approaches that encapsulate production functions as services have shown to be promising in order to increase the flexibility of Cyber-Physical Production Systems. But an existing challenge of such approaches is finding a production plan based on provided functionalities for a demanded product, especially when there is no direct (i.e., syntactic) match between demanded and provided functions. While there is a variety of approaches to production planning, flexible production poses specific requirements that are not covered by existing research. In this contribution, we first capture these requirements for flexible production environments. Afterwards, an overview of current Artificial Intelligence approaches that can be utilized in order to overcome the aforementioned challenges is given. For this purpose, we focus on planning algorithms, but also consider models of production systems that can act as inputs to these algorithms. Approaches from both symbolic AI planning as well as approaches based on Machine Learning are discussed and eventually compared against the requirements. Based on this comparison, a research agenda is derived.-
dc.description.sponsorshipDTEC.bw-
dc.description.sponsorshipInformatik im Maschinenbau-
dc.language.isoeng-
dc.relationKI-basierte Assistenzsystemplattform für komplexe Produktionsprozesse des Maschinen- und Anlagenbaus-
dc.relationEngineering für die KI-basierte Automation in virtuellen und realen Produktionsumgebungen-
dc.relationLabor für die intelligente Leichtbauproduktion-
dc.subjectAI Planning-
dc.subjectdtec.bw-
dc.subjectComputer Science - Artificial Intelligence-
dc.titleA Research Agenda for AI Planning in the Field of Flexible Production Systems-
dc.typeConference Object-
dc.relation.conferenceIEEE 5th International Conference on Industrial Cyber-Physical Systems, ICPS 2022-
dc.identifier.doi10.1109/ICPS51978.2022.9816866-
dc.identifier.scopus2-s2.0-85134477339-
dcterms.bibliographicCitation.booktitleProceedings - 2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems, ICPS 2022-
dc.identifier.urlhttp://arxiv.org/abs/2112.15484v5-
local.submission.typeonly-metadata-
dc.type.conferenceObjectConference Paper-
item.grantfulltextnone-
item.fulltext_sNo Fulltext-
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.openairetypeConference Object-
crisitem.author.deptAutomatisierungstechnik-
crisitem.author.deptInformatik im Maschinenbau-
crisitem.author.deptInformatik im Maschinenbau-
crisitem.author.deptInformatik im Maschinenbau-
crisitem.author.deptInformatik im Maschinenbau-
crisitem.author.deptInformatik im Maschinenbau-
crisitem.author.parentorgFakultät für Maschinenbau und Bauingenieurwesen-
crisitem.author.parentorgFakultät für Maschinenbau und Bauingenieurwesen-
crisitem.author.parentorgFakultät für Maschinenbau und Bauingenieurwesen-
crisitem.author.parentorgFakultät für Maschinenbau und Bauingenieurwesen-
crisitem.author.parentorgFakultät für Maschinenbau und Bauingenieurwesen-
crisitem.author.parentorgFakultät für Maschinenbau und Bauingenieurwesen-
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