Combining machine learning with human knowledge for delivery time estimations

Vorschaubild
Autor:in (Körperschaft)
Publikationsdatum
2022
Typ der Arbeit
Studiengang
Typ
04B - Beitrag Konferenzschrift
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Proceedings of the AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022)
Themenheft
DOI der Originalpublikation
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Reihe / Serie
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Jahrgang / Band
Ausgabe / Nummer
Seiten / Dauer
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Verlag / Herausgebende Institution
American Association for Artificial Intelligence (AAAI) Press
Verlagsort / Veranstaltungsort
Palo Alto
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
Although machine learning algorithms outperform humans in many predictive tasks, their quality depends much on the availability of sufficient and representative training data. On the other hand, humans are capable of making predictions based on “spontaneous” transfers of knowledge from other domains or situations in cases where no directly relevant experiences exist. This can be seen very well in the task of predicting lead times in goods transport, where sudden disruptions or shortages may occur that are not reflected in historical data, but known to a well-informed human. If the variation can be anticipated and more accurate lead times estimated, proactive measures can be taken to decrease the impact. Therefore, we describe three novel approaches for delivery time predictions, combining a machine learning model with human input. The proposed logic covers two phases, learning based on actual delivery data and capturing human knowledge to cover exceptional situations not reflected in historical data. The proposed models and the resulting estimates were evaluated using deliveries from a retail company. It was found that the pure machine learning model delivers better results than a combination of humans and machines. On the one hand, this is caused by the complexity of incorporating human knowledge into the algorithm in a suitable way. On the other hand, it is also due to the tendency of humans to over-generalise the impact of certain events. Thus, although the pure machine learning model delivers superior estimation accuracy than the human-machine combination, our systematic qualitative analysis of the results presents insights for future development in this area.
Schlagwörter
Fachgebiet (DDC)
330 - Wirtschaft
Projekt
Veranstaltung
AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE)
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
ISSN
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
Peer-Review der ganzen Publikation
Open Access-Status
Diamond
Lizenz
'https://creativecommons.org/licenses/by/4.0/'
Zitation
LOCHBRUNNER, Markus und Hans Friedrich WITSCHEL, 2022. Combining machine learning with human knowledge for delivery time estimations. In: Andreas MARTIN, Knut HINKELMANN, Hans-Georg FILL, Aurona GERBER, Doug LENAT, Reinhard STOLLE und Frank VAN HARMELEN (Hrsg.), Proceedings of the AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022). Palo Alto: American Association for Artificial Intelligence (AAAI) Press. 2022. Verfügbar unter: https://doi.org/10.26041/fhnw-10989