@inproceedings{Brand.2023_RLForAdaptiveWorkflows,
  author       = {Florian Brand and
                  Katharina Lott and
                  Lukas Malburg and
                  Maximilian Hoffmann and
                  Ralph Bergmann},
  editor       = {Lukas Malburg and
                  Deepika Verma},
  title        = {{Using Deep Reinforcement Learning for the Adaptation of Semantic Workflows}},
  booktitle    = {Proceedings of the Workshops at the 31st International Conference
                  on Case-Based Reasoning {(ICCBR-WS} 2023) co-located with the 31st
                  International Conference on Case-Based Reasoning {(ICCBR} 2023), Aberdeen,
                  Scotland, UK, July 17, 2023},
  series       = {{CEUR} Workshop Proceedings},
  volume       = {3438},
  pages        = {55--70},
  publisher    = {CEUR-WS.org},
  year         = {2023},
  keywords     = {{Case-Based Reasoning, Semantic Workflows, Deep Learning, Reinforcement Learning}},
  abstract     = {Case-Based Reasoning (CBR) solves new problems by using experience represented by solved cases. The acquisition of adaptation knowledge and its subsequent application remains a classic challenge for CBR applications. In this paper, we present a novel approach for adapting semantic workflows during the reuse phase of the CBR cycle. A reinforcement learning agent is utilized, which applies different actions to change nodes of the workflow. Thereby, changes to the workflow are made by replacing, deleting or adding nodes. The agent is evaluated in a case study outlining its ability to adapt a semantic graph in a smart manufacturing domain. While the approach is detailed for the application in the particular domain, it can be adopted for the usage in other process-oriented domains.},
  url          = {http://www.wi2.uni-trier.de/shared/publications/2023_Brand_RLForAdaptiveWorkflows.pdf}
}

{"_id":"gkMA9J42CjeFsGHEz","bibbaseid":"brand-lott-malburg-hoffmann-bergmann-usingdeepreinforcementlearningfortheadaptationofsemanticworkflows-2023","author_short":["Brand, F.","Lott, K.","Malburg, L.","Hoffmann, M.","Bergmann, R."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["Florian"],"propositions":[],"lastnames":["Brand"],"suffixes":[]},{"firstnames":["Katharina"],"propositions":[],"lastnames":["Lott"],"suffixes":[]},{"firstnames":["Lukas"],"propositions":[],"lastnames":["Malburg"],"suffixes":[]},{"firstnames":["Maximilian"],"propositions":[],"lastnames":["Hoffmann"],"suffixes":[]},{"firstnames":["Ralph"],"propositions":[],"lastnames":["Bergmann"],"suffixes":[]}],"editor":[{"firstnames":["Lukas"],"propositions":[],"lastnames":["Malburg"],"suffixes":[]},{"firstnames":["Deepika"],"propositions":[],"lastnames":["Verma"],"suffixes":[]}],"title":"Using Deep Reinforcement Learning for the Adaptation of Semantic Workflows","booktitle":"Proceedings of the Workshops at the 31st International Conference on Case-Based Reasoning (ICCBR-WS 2023) co-located with the 31st International Conference on Case-Based Reasoning (ICCBR 2023), Aberdeen, Scotland, UK, July 17, 2023","series":"CEUR Workshop Proceedings","volume":"3438","pages":"55–70","publisher":"CEUR-WS.org","year":"2023","keywords":"Case-Based Reasoning, Semantic Workflows, Deep Learning, Reinforcement Learning","abstract":"Case-Based Reasoning (CBR) solves new problems by using experience represented by solved cases. The acquisition of adaptation knowledge and its subsequent application remains a classic challenge for CBR applications. In this paper, we present a novel approach for adapting semantic workflows during the reuse phase of the CBR cycle. A reinforcement learning agent is utilized, which applies different actions to change nodes of the workflow. Thereby, changes to the workflow are made by replacing, deleting or adding nodes. The agent is evaluated in a case study outlining its ability to adapt a semantic graph in a smart manufacturing domain. While the approach is detailed for the application in the particular domain, it can be adopted for the usage in other process-oriented domains.","url":"http://www.wi2.uni-trier.de/shared/publications/2023_Brand_RLForAdaptiveWorkflows.pdf","bibtex":"@inproceedings{Brand.2023_RLForAdaptiveWorkflows,\n author = {Florian Brand and\n Katharina Lott and\n Lukas Malburg and\n Maximilian Hoffmann and\n Ralph Bergmann},\n editor = {Lukas Malburg and\n Deepika Verma},\n title = {{Using Deep Reinforcement Learning for the Adaptation of Semantic Workflows}},\n booktitle = {Proceedings of the Workshops at the 31st International Conference\n on Case-Based Reasoning {(ICCBR-WS} 2023) co-located with the 31st\n International Conference on Case-Based Reasoning {(ICCBR} 2023), Aberdeen,\n Scotland, UK, July 17, 2023},\n series = {{CEUR} Workshop Proceedings},\n volume = {3438},\n pages = {55--70},\n publisher = {CEUR-WS.org},\n year = {2023},\n keywords = {{Case-Based Reasoning, Semantic Workflows, Deep Learning, Reinforcement Learning}},\n abstract = {Case-Based Reasoning (CBR) solves new problems by using experience represented by solved cases. The acquisition of adaptation knowledge and its subsequent application remains a classic challenge for CBR applications. In this paper, we present a novel approach for adapting semantic workflows during the reuse phase of the CBR cycle. A reinforcement learning agent is utilized, which applies different actions to change nodes of the workflow. Thereby, changes to the workflow are made by replacing, deleting or adding nodes. The agent is evaluated in a case study outlining its ability to adapt a semantic graph in a smart manufacturing domain. While the approach is detailed for the application in the particular domain, it can be adopted for the usage in other process-oriented domains.},\n url = {http://www.wi2.uni-trier.de/shared/publications/2023_Brand_RLForAdaptiveWorkflows.pdf}\n}\n\n","author_short":["Brand, F.","Lott, K.","Malburg, L.","Hoffmann, M.","Bergmann, R."],"editor_short":["Malburg, L.","Verma, D."],"key":"Brand.2023_RLForAdaptiveWorkflows","id":"Brand.2023_RLForAdaptiveWorkflows","bibbaseid":"brand-lott-malburg-hoffmann-bergmann-usingdeepreinforcementlearningfortheadaptationofsemanticworkflows-2023","role":"author","urls":{"Paper":"http://www.wi2.uni-trier.de/shared/publications/2023_Brand_RLForAdaptiveWorkflows.pdf"},"keyword":["Case-Based Reasoning","Semantic Workflows","Deep Learning","Reinforcement Learning"],"metadata":{"authorlinks":{}},"downloads":17},"bibtype":"inproceedings","biburl":"https://web.wi2.uni-trier.de/publications/WI2Publikationen.bib","dataSources":["CnwPa99ZchEF4SZgh","MSp3DzP4ToPojqkFy","Td7BJ334QwxWK4vLW","J3orK6zvpR7d8vDmC"],"keywords":["case-based reasoning","semantic workflows","deep learning","reinforcement learning"],"search_terms":["using","deep","reinforcement","learning","adaptation","semantic","workflows","brand","lott","malburg","hoffmann","bergmann"],"title":"Using Deep Reinforcement Learning for the Adaptation of Semantic Workflows","year":2023,"downloads":17}