Publications

@inproceedings{Brand.2023,

title = {Using Deep Reinforcement Learning for the Adaptation of Semantic Workflows},

author = {Florian Brand and Katharina Lott and Lukas Malburg and Maximilian Hoffmann and Ralph Bergmann},

editor = {Lukas Malburg and Deepika Verma},

url = {urlhttp://www.wi2.uni-trier.de/shared/publications/2023_Brand_RLForAdaptiveWorkflows.pdf},

year  = {2023},

date = {2023-01-01},

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},

volume = {3438},

pages = {55–70},

publisher = {CEUR-WS.org},

series = {CEUR Workshop Proceedings},

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.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Hoffmann.2022d,

title = {ProGAN: Toward a~Framework for~Process Monitoring and~Flexibility by~Change via~Generative Adversarial Networks},

author = {Maximilian Hoffmann and Lukas Malburg and Ralph Bergmann},

editor = {Andrea Marrella and Barbara Weber},

isbn = {978-3-030-94343-1},

year  = {2022},

date = {2022-01-01},

booktitle = {Business Process Management Workshops},

pages = {43--55},

publisher = {Springer International Publishing},

address = {Cham},

abstract = {Monitoring the state of currently running processes and reacting to deviations during runtime is a key challenge in Business Process Management (BPM). The MAPE-K control loop describes four phases for approaching this challenge: Monitor, Analyze, Plan, Execute. In this paper, we present the ProGAN framework, an idea of an approach for implementing the monitor, analyze, and plan phases of MAPE-K. For this purpose, we leverage a deep learning architecture that builds upon Generative Adversarial Networks (GANs): The discriminator is used for monitoring the process in its environment by using sensor data and for detecting deviations w.r.t. the desired process state (monitor phase). The generator is used afterwards for analyzing the detected deviation and its symptoms as well as for adapting the current process to resolve the deviation and to restore the desired state. Both components are trained together by utilizing each other's feedback in a self-supervised way. We demonstrate the application of our approach for an exemplary scenario in the manufacturing domain.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Malburg.2021,

title = {Improving Similarity-Based Retrieval Efficiency by Using Graphic Processing Units in Case-Based Reasoning},

author = {Lukas Malburg and Maximilian Hoffmann and Simon Trumm and Ralph Bergmann},

url = {http://www.wi2.uni-trier.de/shared/publications/2021_MalburgEtAl_ImprovingRetrievalByGPUs.pdf},

doi = {10.32473/flairs.v34i1.128345},

year  = {2021},

date = {2021-01-01},

booktitle = {Proceedings of the 34th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2021, North Miami Beach, Florida, USA},

abstract = {The accelerated growth of available data causes case bases of increasing sizes and thus lowers efficiency during the case retrieval phase in Case-Based Reasoning (CBR) systems. Even though, many complex and data-intensive tasks are solved by using Graphic Processing Units (GPUs), its application in CBR research has yet to advance past the early stage phase. In this paper, we present an approach to use CUDA-compatible GPUs for similarity assessment of structural, feature vector based cases. Our approach supports several syntactic and semantic similarity measures and is implemented in the open-source case-based reasoning framework ProCAKE. When comparing to current retrieval techniques that calculate similarities on the CPU, our GPU-based approach outperforms them by a factor of up to 37. In addition, our evaluation indicates that the performance gains increase with higher case complexity.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}