Learning Planning Model for Semantic Process Compensation

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dc.contributor.authorAlelaimat, Ahmad
dc.contributor.authorSantiputri, Metta
dc.contributor.authorGou, Yingzhi
dc.contributor.authorGhose, Aditya
dc.date.accessioned2023-06-28T05:29:43Z
dc.date.available2023-06-28T05:29:43Z
dc.date.issued2018-03-03
dc.description.abstrakRecent advancements in business process conformance anal- ysis have shown that the detection of non-conformance states can be learned with discovering inconsistencies between process models and their historical execution logs, despite their real behaviour. A key chal- lenge in managing business processes is compensating non-conformance states. The concentration of this work is on the hardest aspect of the chal- lenge, where the process might be structurally conformant, but it does not achieve an effect conform to what is required by design. In this work, we propose learning and planning model to address the compensation of semantically non-conformance states. Our work departs from the inte- gration of two well-known AI paradigms, Machine Learning (ML) and Automated Planning (AP). Learning model is divided into two models to address two planning problems: learning predictive model that pro- vides the planner with the ability to respond to violation points during the execution of the process model, and instance-based learning model that provides the planer with a compensation based on the nearest class when there are no compensations perfectly fit to the violation point.en_US
dc.identifier.citationAlelaimat, A., Santipuri, M., Gou, Y., Ghose, A. (2018). Learning Planning Model for Semantic Process Compensation. In: Beheshti, A., Hashmi, M., Dong, H., Zhang, W. (eds) Service Research and Innovation. ASSRI ASSRI 2015 2017. Lecture Notes in Business Information Processing, vol 234. Springer, Cham. https://doi.org/10.1007/978-3-319-76587-7_3en_US
dc.identifier.isbn978-3-319-76587-7
dc.identifier.issn1865-1348
dc.identifier.otherhttps://doi.org/10.1007/978-3-319-76587-7
dc.identifier.urihttps://repository.polibatam.ac.id/xmlui/handle/123456789/1717
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofseriesProceedings of the 6th Australian Symposium on Service Research and Innovation (ASSRI 2017);
dc.relation.ispartofseriesLecture Notes in Business Information Processing;volume 234
dc.subjectSemantic process compensationen_US
dc.subjectLearning modelen_US
dc.subjectAutomated planningen_US
dc.titleLearning Planning Model for Semantic Process Compensationen_US
dc.typeBook chapteren_US

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