--- _id: '45780' author: - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede citation: ama: 'Tornede A. Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions.; 2023. doi:10.17619/UNIPB/1-1780 ' apa: 'Tornede, A. (2023). Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions. https://doi.org/10.17619/UNIPB/1-1780 ' bibtex: '@book{Tornede_2023, title={Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions}, DOI={10.17619/UNIPB/1-1780 }, author={Tornede, Alexander}, year={2023} }' chicago: 'Tornede, Alexander. Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions, 2023. https://doi.org/10.17619/UNIPB/1-1780 .' ieee: 'A. Tornede, Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions. 2023.' mla: 'Tornede, Alexander. Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions. 2023, doi:10.17619/UNIPB/1-1780 .' short: 'A. Tornede, Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions, 2023.' date_created: 2023-06-27T05:20:14Z date_updated: 2023-08-04T06:01:49Z ddc: - '006' department: - _id: '355' doi: '10.17619/UNIPB/1-1780 ' file: - access_level: open_access content_type: application/pdf creator: ahetzer date_created: 2023-07-24T08:40:35Z date_updated: 2023-07-24T08:42:01Z file_id: '46118' file_name: dissertation_alexander_tornede_final_publishing_compressed.pdf file_size: 4300633 relation: main_file title: ' Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions' file_date_updated: 2023-07-24T08:42:01Z has_accepted_license: '1' language: - iso: eng oa: '1' project: - _id: '10' grant_number: '160364472' name: 'SFB 901 - B2: Konfiguration und Bewertung (B02)' - _id: '3' name: 'SFB 901 - B: SFB 901 - Project Area B' - _id: '1' grant_number: '160364472' name: 'SFB 901: SFB 901: On-The-Fly Computing - Individualisierte IT-Dienstleistungen in dynamischen Märkten ' status: public supervisor: - first_name: Eyke full_name: Hüllermeier, Eyke last_name: Hüllermeier title: 'Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions' type: dissertation user_id: '15504' year: '2023' ... --- _id: '21600' abstract: - lang: eng text: Many problems in science and engineering require an efficient numerical approximation of integrals or solutions to differential equations. For systems with rapidly changing dynamics, an equidistant discretization is often inadvisable as it results in prohibitively large errors or computational effort. To this end, adaptive schemes, such as solvers based on Runge–Kutta pairs, have been developed which adapt the step size based on local error estimations at each step. While the classical schemes apply very generally and are highly efficient on regular systems, they can behave suboptimally when an inefficient step rejection mechanism is triggered by structurally complex systems such as chaotic systems. To overcome these issues, we propose a method to tailor numerical schemes to the problem class at hand. This is achieved by combining simple, classical quadrature rules or ODE solvers with data-driven time-stepping controllers. Compared with learning solution operators to ODEs directly, it generalizes better to unseen initial data as our approach employs classical numerical schemes as base methods. At the same time it can make use of identified structures of a problem class and, therefore, outperforms state-of-the-art adaptive schemes. Several examples demonstrate superior efficiency. Source code is available at https://github.com/lueckem/quadrature-ML. author: - first_name: Michael full_name: Dellnitz, Michael last_name: Dellnitz - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Marvin full_name: Lücke, Marvin last_name: Lücke - first_name: Sina full_name: Ober-Blöbaum, Sina id: '16494' last_name: Ober-Blöbaum - first_name: Christian full_name: Offen, Christian id: '85279' last_name: Offen orcid: 0000-0002-5940-8057 - first_name: Sebastian full_name: Peitz, Sebastian id: '47427' last_name: Peitz orcid: 0000-0002-3389-793X - first_name: Karlson full_name: Pfannschmidt, Karlson id: '13472' last_name: Pfannschmidt orcid: 0000-0001-9407-7903 citation: ama: Dellnitz M, Hüllermeier E, Lücke M, et al. Efficient time stepping for numerical integration using reinforcement  learning. SIAM Journal on Scientific Computing. 2023;45(2):A579-A595. doi:10.1137/21M1412682 apa: Dellnitz, M., Hüllermeier, E., Lücke, M., Ober-Blöbaum, S., Offen, C., Peitz, S., & Pfannschmidt, K. (2023). Efficient time stepping for numerical integration using reinforcement  learning. SIAM Journal on Scientific Computing, 45(2), A579–A595. https://doi.org/10.1137/21M1412682 bibtex: '@article{Dellnitz_Hüllermeier_Lücke_Ober-Blöbaum_Offen_Peitz_Pfannschmidt_2023, title={Efficient time stepping for numerical integration using reinforcement  learning}, volume={45}, DOI={10.1137/21M1412682}, number={2}, journal={SIAM Journal on Scientific Computing}, author={Dellnitz, Michael and Hüllermeier, Eyke and Lücke, Marvin and Ober-Blöbaum, Sina and Offen, Christian and Peitz, Sebastian and Pfannschmidt, Karlson}, year={2023}, pages={A579–A595} }' chicago: 'Dellnitz, Michael, Eyke Hüllermeier, Marvin Lücke, Sina Ober-Blöbaum, Christian Offen, Sebastian Peitz, and Karlson Pfannschmidt. “Efficient Time Stepping for Numerical Integration Using Reinforcement  Learning.” SIAM Journal on Scientific Computing 45, no. 2 (2023): A579–95. https://doi.org/10.1137/21M1412682.' ieee: 'M. Dellnitz et al., “Efficient time stepping for numerical integration using reinforcement  learning,” SIAM Journal on Scientific Computing, vol. 45, no. 2, pp. A579–A595, 2023, doi: 10.1137/21M1412682.' mla: Dellnitz, Michael, et al. “Efficient Time Stepping for Numerical Integration Using Reinforcement  Learning.” SIAM Journal on Scientific Computing, vol. 45, no. 2, 2023, pp. A579–95, doi:10.1137/21M1412682. short: M. Dellnitz, E. Hüllermeier, M. Lücke, S. Ober-Blöbaum, C. Offen, S. Peitz, K. Pfannschmidt, SIAM Journal on Scientific Computing 45 (2023) A579–A595. date_created: 2021-04-09T07:59:19Z date_updated: 2023-08-25T09:24:50Z ddc: - '510' department: - _id: '101' - _id: '636' - _id: '355' - _id: '655' doi: 10.1137/21M1412682 external_id: arxiv: - arXiv:2104.03562 has_accepted_license: '1' intvolume: ' 45' issue: '2' language: - iso: eng main_file_link: - url: https://epubs.siam.org/doi/reader/10.1137/21M1412682 page: A579-A595 publication: SIAM Journal on Scientific Computing publication_status: published related_material: link: - description: GitHub relation: software url: https://github.com/lueckem/quadrature-ML status: public title: Efficient time stepping for numerical integration using reinforcement learning type: journal_article user_id: '47427' volume: 45 year: '2023' ... --- _id: '24382' author: - first_name: Karina full_name: Gevers, Karina id: '83151' last_name: Gevers - first_name: Volker full_name: Schöppner, Volker id: '20530' last_name: Schöppner - first_name: Eyke full_name: Hüllermeier, Eyke last_name: Hüllermeier citation: ama: 'Gevers K, Schöppner V, Hüllermeier E. Heated tool butt welding of two different materials –  Established methods versus artificial intelligence. In: ; 2021.' apa: Gevers, K., Schöppner, V., & Hüllermeier, E. (2021). Heated tool butt welding of two different materials –  Established methods versus artificial intelligence. International Institute of Welding, online. bibtex: '@inproceedings{Gevers_Schöppner_Hüllermeier_2021, title={Heated tool butt welding of two different materials –  Established methods versus artificial intelligence}, author={Gevers, Karina and Schöppner, Volker and Hüllermeier, Eyke}, year={2021} }' chicago: Gevers, Karina, Volker Schöppner, and Eyke Hüllermeier. “Heated Tool Butt Welding of Two Different Materials –  Established Methods versus Artificial Intelligence,” 2021. ieee: K. Gevers, V. Schöppner, and E. Hüllermeier, “Heated tool butt welding of two different materials –  Established methods versus artificial intelligence,” presented at the International Institute of Welding, online, 2021. mla: Gevers, Karina, et al. Heated Tool Butt Welding of Two Different Materials –  Established Methods versus Artificial Intelligence. 2021. short: 'K. Gevers, V. Schöppner, E. Hüllermeier, in: 2021.' conference: end_date: 2021-07-14 location: online name: International Institute of Welding start_date: 2021-07-12 date_created: 2021-09-14T11:34:31Z date_updated: 2022-01-06T06:56:19Z department: - _id: '367' - _id: '355' - _id: '321' language: - iso: eng status: public title: Heated tool butt welding of two different materials – Established methods versus artificial intelligence type: conference user_id: '83151' year: '2021' ... --- _id: '21004' abstract: - lang: eng text: 'Automated machine learning (AutoML) supports the algorithmic construction and data-specific customization of machine learning pipelines, including the selection, combination, and parametrization of machine learning algorithms as main constituents. Generally speaking, AutoML approaches comprise two major components: a search space model and an optimizer for traversing the space. Recent approaches have shown impressive results in the realm of supervised learning, most notably (single-label) classification (SLC). Moreover, first attempts at extending these approaches towards multi-label classification (MLC) have been made. While the space of candidate pipelines is already huge in SLC, the complexity of the search space is raised to an even higher power in MLC. One may wonder, therefore, whether and to what extent optimizers established for SLC can scale to this increased complexity, and how they compare to each other. This paper makes the following contributions: First, we survey existing approaches to AutoML for MLC. Second, we augment these approaches with optimizers not previously tried for MLC. Third, we propose a benchmarking framework that supports a fair and systematic comparison. Fourth, we conduct an extensive experimental study, evaluating the methods on a suite of MLC problems. We find a grammar-based best-first search to compare favorably to other optimizers.' author: - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Wever MD, Tornede A, Mohr F, Hüllermeier E. AutoML for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence. Published online 2021:1-1. doi:10.1109/tpami.2021.3051276' apa: 'Wever, M. D., Tornede, A., Mohr, F., & Hüllermeier, E. (2021). AutoML for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2021.3051276' bibtex: '@article{Wever_Tornede_Mohr_Hüllermeier_2021, title={AutoML for Multi-Label Classification: Overview and Empirical Evaluation}, DOI={10.1109/tpami.2021.3051276}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, author={Wever, Marcel Dominik and Tornede, Alexander and Mohr, Felix and Hüllermeier, Eyke}, year={2021}, pages={1–1} }' chicago: 'Wever, Marcel Dominik, Alexander Tornede, Felix Mohr, and Eyke Hüllermeier. “AutoML for Multi-Label Classification: Overview and Empirical Evaluation.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 1–1. https://doi.org/10.1109/tpami.2021.3051276.' ieee: 'M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “AutoML for Multi-Label Classification: Overview and Empirical Evaluation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1–1, 2021, doi: 10.1109/tpami.2021.3051276.' mla: 'Wever, Marcel Dominik, et al. “AutoML for Multi-Label Classification: Overview and Empirical Evaluation.” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, pp. 1–1, doi:10.1109/tpami.2021.3051276.' short: M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, IEEE Transactions on Pattern Analysis and Machine Intelligence (2021) 1–1. date_created: 2021-01-16T14:48:13Z date_updated: 2022-01-06T06:54:42Z department: - _id: '34' - _id: '355' - _id: '26' doi: 10.1109/tpami.2021.3051276 keyword: - Automated Machine Learning - Multi Label Classification - Hierarchical Planning - Bayesian Optimization language: - iso: eng page: 1-1 project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: IEEE Transactions on Pattern Analysis and Machine Intelligence publication_identifier: issn: - 0162-8828 - 2160-9292 - 1939-3539 publication_status: published status: public title: 'AutoML for Multi-Label Classification: Overview and Empirical Evaluation' type: journal_article user_id: '5786' year: '2021' ... --- _id: '21092' abstract: - lang: eng text: "Automated Machine Learning (AutoML) seeks to automatically find so-called machine learning pipelines that maximize the prediction performance when being used to train a model on a given dataset. One of the main and yet open challenges in AutoML is an effective use of computational resources: An AutoML process involves the evaluation of many candidate pipelines, which are costly but often ineffective because they are canceled due to a timeout.\r\nIn this paper, we present an approach to predict the runtime of two-step machine learning pipelines with up to one pre-processor, which can be used to anticipate whether or not a pipeline will time out. Separate runtime models are trained offline for each algorithm that may be used in a pipeline, and an overall prediction is derived from these models. We empirically show that the approach increases successful evaluations made by an AutoML tool while preserving or even improving on the previously best solutions." author: - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: Mohr F, Wever MD, Tornede A, Hüllermeier E. Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. apa: Mohr, F., Wever, M. D., Tornede, A., & Hüllermeier, E. (n.d.). Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. bibtex: '@article{Mohr_Wever_Tornede_Hüllermeier, title={Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, publisher={IEEE}, author={Mohr, Felix and Wever, Marcel Dominik and Tornede, Alexander and Hüllermeier, Eyke} }' chicago: Mohr, Felix, Marcel Dominik Wever, Alexander Tornede, and Eyke Hüllermeier. “Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning.” IEEE Transactions on Pattern Analysis and Machine Intelligence, n.d. ieee: F. Mohr, M. D. Wever, A. Tornede, and E. Hüllermeier, “Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence. mla: Mohr, Felix, et al. “Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning.” IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE. short: F. Mohr, M.D. Wever, A. Tornede, E. Hüllermeier, IEEE Transactions on Pattern Analysis and Machine Intelligence (n.d.). date_created: 2021-01-27T13:45:52Z date_updated: 2022-01-06T06:54:45Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: IEEE Transactions on Pattern Analysis and Machine Intelligence publication_status: accepted publisher: IEEE status: public title: Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning type: journal_article user_id: '5786' year: '2021' ... --- _id: '21535' author: - first_name: Viktor full_name: Bengs, Viktor last_name: Bengs - first_name: Róbert full_name: Busa-Fekete, Róbert last_name: Busa-Fekete - first_name: Adil full_name: El Mesaoudi-Paul, Adil last_name: El Mesaoudi-Paul - first_name: Eyke full_name: Hüllermeier, Eyke last_name: Hüllermeier citation: ama: 'Bengs V, Busa-Fekete R, El Mesaoudi-Paul A, Hüllermeier E. Preference-based Online Learning with Dueling Bandits: A Survey. Journal of Machine Learning Research. 2021;22(7):1-108.' apa: 'Bengs, V., Busa-Fekete, R., El Mesaoudi-Paul, A., & Hüllermeier, E. (2021). Preference-based Online Learning with Dueling Bandits: A Survey. Journal of Machine Learning Research, 22(7), 1–108.' bibtex: '@article{Bengs_Busa-Fekete_El Mesaoudi-Paul_Hüllermeier_2021, title={Preference-based Online Learning with Dueling Bandits: A Survey}, volume={22}, number={7}, journal={Journal of Machine Learning Research}, author={Bengs, Viktor and Busa-Fekete, Róbert and El Mesaoudi-Paul, Adil and Hüllermeier, Eyke}, year={2021}, pages={1–108} }' chicago: 'Bengs, Viktor, Róbert Busa-Fekete, Adil El Mesaoudi-Paul, and Eyke Hüllermeier. “Preference-Based Online Learning with Dueling Bandits: A Survey.” Journal of Machine Learning Research 22, no. 7 (2021): 1–108.' ieee: 'V. Bengs, R. Busa-Fekete, A. El Mesaoudi-Paul, and E. Hüllermeier, “Preference-based Online Learning with Dueling Bandits: A Survey,” Journal of Machine Learning Research, vol. 22, no. 7, pp. 1–108, 2021.' mla: 'Bengs, Viktor, et al. “Preference-Based Online Learning with Dueling Bandits: A Survey.” Journal of Machine Learning Research, vol. 22, no. 7, 2021, pp. 1–108.' short: V. Bengs, R. Busa-Fekete, A. El Mesaoudi-Paul, E. Hüllermeier, Journal of Machine Learning Research 22 (2021) 1–108. date_created: 2021-03-18T11:15:38Z date_updated: 2022-01-06T06:55:03Z department: - _id: '34' - _id: '7' - _id: '355' intvolume: ' 22' issue: '7' language: - iso: eng page: 1-108 publication: Journal of Machine Learning Research quality_controlled: '1' status: public title: 'Preference-based Online Learning with Dueling Bandits: A Survey' type: journal_article user_id: '76599' volume: 22 year: '2021' ... --- _id: '21570' author: - first_name: Tanja full_name: Tornede, Tanja id: '40795' last_name: Tornede - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Tornede T, Tornede A, Wever MD, Hüllermeier E. Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance. In: Proceedings of the Genetic and Evolutionary Computation Conference. ; 2021.' apa: Tornede, T., Tornede, A., Wever, M. D., & Hüllermeier, E. (2021). Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance. Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference. bibtex: '@inproceedings{Tornede_Tornede_Wever_Hüllermeier_2021, title={Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference}, author={Tornede, Tanja and Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2021} }' chicago: Tornede, Tanja, Alexander Tornede, Marcel Dominik Wever, and Eyke Hüllermeier. “Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance.” In Proceedings of the Genetic and Evolutionary Computation Conference, 2021. ieee: T. Tornede, A. Tornede, M. D. Wever, and E. Hüllermeier, “Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance,” presented at the Genetic and Evolutionary Computation Conference, 2021. mla: Tornede, Tanja, et al. “Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance.” Proceedings of the Genetic and Evolutionary Computation Conference, 2021. short: 'T. Tornede, A. Tornede, M.D. Wever, E. Hüllermeier, in: Proceedings of the Genetic and Evolutionary Computation Conference, 2021.' conference: end_date: 2021-07-14 name: Genetic and Evolutionary Computation Conference start_date: 2021-07-10 date_created: 2021-03-26T09:14:19Z date_updated: 2022-01-06T06:55:06Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 - _id: '52' name: Computing Resources Provided by the Paderborn Center for Parallel Computing publication: Proceedings of the Genetic and Evolutionary Computation Conference status: public title: Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance type: conference user_id: '5786' year: '2021' ... --- _id: '23779' abstract: - lang: ger text: "Produktentstehung (PE) bezieht sich auf den Prozess der Planung und Entwicklung eines Produkts sowie der damit verbundenen Dienstleistungen von der ersten Idee bis zur Herstellung und zum Vertrieb. Während dieses Prozesses gibt es zahlreiche Aufgaben, die von menschlichem Fachwissen abhängen und typischerweise von erfahrenen Experten übernommen werden. Da sich das Feld der Künstlichen Intelligenz (KI) immer weiterentwickelt und seinen Weg in den Fertigungssektor findet, gibt es viele Möglichkeiten für eine Anwendung von KI, um bei der Lösung der oben genannten Aufgaben zu helfen. In diesem Paper geben wir einen umfassenden Überblick über den aktuellen Stand der Technik des Einsatzes von KI in der PE. \r\nIm Detail analysieren wir 40 bestehende Surveys zu KI in der PE und 94 Case Studies, um herauszufinden, welche Bereiche der PE von der aktuellen Forschung in diesem Bereich vorrangig adressiert werden, wie ausgereift die diskutierten KI-Methoden sind und inwieweit datenzentrierte Ansätze in der aktuellen Forschung genutzt werden." - lang: eng text: "Product Creation (PC) refers to the process of planning and developing a product as well as related services from the initial idea until manufacturing and distribution. Throughout this process, there are numerous tasks that depend on human expertise and are typically undertaken by experienced practitioners. As the field of Artificial Intelligence (AI) continues to evolve and finds its way into the manufacturing sector, there exist many possibilities for an application of AI in order to assist in solving aforementioned tasks. In this work, we provide a comprehensive overview of the current state of the art of the use of AI in PC. \r\nIn detail, we analyze 40 existing surveys on AI in PC and 94 case studies in order to find out which areas of PC are primarily addressed by current research in this field, how mature the discussed AI methods are, and to which extent data-centric approaches are utilized in current research." author: - first_name: Ruslan full_name: Bernijazov, Ruslan last_name: Bernijazov - first_name: Alexander full_name: Dicks, Alexander last_name: Dicks - first_name: Roman full_name: Dumitrescu, Roman id: '16190' last_name: Dumitrescu - first_name: Marc full_name: Foullois, Marc last_name: Foullois - first_name: Jonas Manuel full_name: Hanselle, Jonas Manuel id: '43980' last_name: Hanselle orcid: 0000-0002-1231-4985 - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Gökce full_name: Karakaya, Gökce last_name: Karakaya - first_name: Patrick full_name: Ködding, Patrick id: '45402' last_name: Ködding - first_name: Volker full_name: Lohweg, Volker last_name: Lohweg - first_name: Manuel full_name: Malatyali, Manuel id: '41265' last_name: Malatyali - first_name: Friedhelm full_name: Meyer auf der Heide, Friedhelm id: '15523' last_name: Meyer auf der Heide - first_name: Melina full_name: Panzner, Melina last_name: Panzner - first_name: Christian full_name: Soltenborn, Christian id: '1737' last_name: Soltenborn orcid: 0000-0002-0342-8227 citation: ama: 'Bernijazov R, Dicks A, Dumitrescu R, et al. A Meta-Review on Artificial Intelligence in Product Creation. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21). ; 2021.' apa: Bernijazov, R., Dicks, A., Dumitrescu, R., Foullois, M., Hanselle, J. M., Hüllermeier, E., Karakaya, G., Ködding, P., Lohweg, V., Malatyali, M., Meyer auf der Heide, F., Panzner, M., & Soltenborn, C. (2021). A Meta-Review on Artificial Intelligence in Product Creation. Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21). 30th International Joint Conference on Artificial Intelligence (IJCAI 2021) - Workshop “AI and Product Design,” Montreal, Kanada. bibtex: '@inproceedings{Bernijazov_Dicks_Dumitrescu_Foullois_Hanselle_Hüllermeier_Karakaya_Ködding_Lohweg_Malatyali_et al._2021, title={A Meta-Review on Artificial Intelligence in Product Creation}, booktitle={Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21)}, author={Bernijazov, Ruslan and Dicks, Alexander and Dumitrescu, Roman and Foullois, Marc and Hanselle, Jonas Manuel and Hüllermeier, Eyke and Karakaya, Gökce and Ködding, Patrick and Lohweg, Volker and Malatyali, Manuel and et al.}, year={2021} }' chicago: Bernijazov, Ruslan, Alexander Dicks, Roman Dumitrescu, Marc Foullois, Jonas Manuel Hanselle, Eyke Hüllermeier, Gökce Karakaya, et al. “A Meta-Review on Artificial Intelligence in Product Creation.” In Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21), 2021. ieee: R. Bernijazov et al., “A Meta-Review on Artificial Intelligence in Product Creation,” presented at the 30th International Joint Conference on Artificial Intelligence (IJCAI 2021) - Workshop “AI and Product Design,” Montreal, Kanada, 2021. mla: Bernijazov, Ruslan, et al. “A Meta-Review on Artificial Intelligence in Product Creation.” Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21), 2021. short: 'R. Bernijazov, A. Dicks, R. Dumitrescu, M. Foullois, J.M. Hanselle, E. Hüllermeier, G. Karakaya, P. Ködding, V. Lohweg, M. Malatyali, F. Meyer auf der Heide, M. Panzner, C. Soltenborn, in: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21), 2021.' conference: end_date: 2021-08-26 location: Montreal, Kanada name: 30th International Joint Conference on Artificial Intelligence (IJCAI 2021) - Workshop "AI and Product Design" start_date: 2021-08-19 date_created: 2021-09-06T08:23:45Z date_updated: 2022-01-06T06:55:59Z department: - _id: '63' - _id: '563' - _id: '355' - _id: '241' keyword: - Artificial Intelligence Product Creation Literature Review language: - iso: eng main_file_link: - url: https://www.hsu-hh.de/imb/wp-content/uploads/sites/677/2021/08/A-Meta-Review-on-Artificial-Intelligence-in-Product-Creation.pdf publication: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21) publication_status: epub_ahead quality_controlled: '1' status: public title: A Meta-Review on Artificial Intelligence in Product Creation type: conference user_id: '15415' year: '2021' ... --- _id: '22913' author: - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Alexander full_name: Tornede, Alexander id: '38209' last_name: Tornede - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' citation: ama: 'Hüllermeier E, Mohr F, Tornede A, Wever MD. Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. In: ; 2021.' apa: Hüllermeier, E., Mohr, F., Tornede, A., & Wever, M. D. (2021). Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. ECML/PKDD Workshop on Automating Data Science, Bilbao (Virtual). bibtex: '@inproceedings{Hüllermeier_Mohr_Tornede_Wever_2021, title={Automated Machine Learning, Bounded Rationality, and Rational Metareasoning}, author={Hüllermeier, Eyke and Mohr, Felix and Tornede, Alexander and Wever, Marcel Dominik}, year={2021} }' chicago: Hüllermeier, Eyke, Felix Mohr, Alexander Tornede, and Marcel Dominik Wever. “Automated Machine Learning, Bounded Rationality, and Rational Metareasoning,” 2021. ieee: E. Hüllermeier, F. Mohr, A. Tornede, and M. D. Wever, “Automated Machine Learning, Bounded Rationality, and Rational Metareasoning,” presented at the ECML/PKDD Workshop on Automating Data Science, Bilbao (Virtual), 2021. mla: Hüllermeier, Eyke, et al. Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. 2021. short: 'E. Hüllermeier, F. Mohr, A. Tornede, M.D. Wever, in: 2021.' conference: end_date: 2021-09-17 location: Bilbao (Virtual) name: ECML/PKDD Workshop on Automating Data Science start_date: 2021-09-13 date_created: 2021-08-02T07:46:29Z date_updated: 2022-01-06T06:55:43Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 quality_controlled: '1' status: public title: Automated Machine Learning, Bounded Rationality, and Rational Metareasoning type: conference user_id: '5786' year: '2021' ... --- _id: '22914' author: - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' citation: ama: 'Mohr F, Wever MD. Replacing the Ex-Def Baseline in AutoML by Naive AutoML. In: ; 2021.' apa: Mohr, F., & Wever, M. D. (2021). Replacing the Ex-Def Baseline in AutoML by Naive AutoML. 8th ICML Workshop on Automated Machine Learning, Virtual. bibtex: '@inproceedings{Mohr_Wever_2021, title={Replacing the Ex-Def Baseline in AutoML by Naive AutoML}, author={Mohr, Felix and Wever, Marcel Dominik}, year={2021} }' chicago: Mohr, Felix, and Marcel Dominik Wever. “Replacing the Ex-Def Baseline in AutoML by Naive AutoML,” 2021. ieee: F. Mohr and M. D. Wever, “Replacing the Ex-Def Baseline in AutoML by Naive AutoML,” presented at the 8th ICML Workshop on Automated Machine Learning, Virtual, 2021. mla: Mohr, Felix, and Marcel Dominik Wever. Replacing the Ex-Def Baseline in AutoML by Naive AutoML. 2021. short: 'F. Mohr, M.D. Wever, in: 2021.' conference: end_date: 2021-07-23 location: Virtual name: 8th ICML Workshop on Automated Machine Learning start_date: 2021-07-23 date_created: 2021-08-02T07:48:07Z date_updated: 2022-01-06T06:55:43Z department: - _id: '34' - _id: '355' - _id: '26' language: - iso: eng status: public title: Replacing the Ex-Def Baseline in AutoML by Naive AutoML type: conference user_id: '5786' year: '2021' ...