Publications
2025
Hotz, Maxim; Malburg, Lukas; Bergmann, Ralph
Advanced Search Techniques for Determining Optimal Sequences of Adaptation Rules in Process-Oriented Case-Based Reasoning Proceedings Article
In: Bichindaritz, Isabelle; Lopez, Beatriz (Ed.): Case-Based Reasoning Research and Development - 33rd International Conference, ICCBR 2025, Biarritz, France, June 30 - July 3, 2025, Proceedings, pp. 236–251, Springer., 2025.
Abstract | Links | BibTeX | Tags: Adaptive Workflow Management, Constrained Optimization Planning, Genetic Algorithms, Process-Oriented Case-Based Reasoning, Rule-Based Adaptation
@inproceedings{Hotz_AdvancedSearchForRules_2025,
title = {Advanced Search Techniques for Determining Optimal Sequences of Adaptation Rules in Process-Oriented Case-Based Reasoning},
author = {Maxim Hotz and Lukas Malburg and Ralph Bergmann},
editor = {Isabelle Bichindaritz and Beatriz Lopez},
url = {https://www.wi2.uni-trier.de/shared/publications/2025_ICCBR_AdvancedSearchForRules_HotzEtAl.pdf},
doi = {10.1007/978-3-031-96559-3_16},
year = {2025},
date = {2025-01-01},
booktitle = {Case-Based Reasoning Research and Development - 33rd International Conference, ICCBR 2025, Biarritz, France, June 30 - July 3, 2025, Proceedings},
volume = {15662},
pages = {236–251},
publisher = {Springer.},
series = {Lecture Notes in Computer Science},
abstract = {The application of adaptation knowledge still poses a major challenge in modern Case-Based Reasoning (CBR) systems. This is especially the case in the subdomain of Process-Oriented Case-Based Reasoning (POCBR), where cases represent procedural experimental knowledge. Current adaptation methods in this field make use of proprietary local search techniques to apply adaptation knowledge, which is inefficient and does not allow exploitation of advanced search and optimization techniques. Therefore, this work presents an approach to transform rule-based adaptation into a planning problem that is solvable with both Constrained Optimization Planning (COP) and Genetic Algorithms (GA). The results of an experimental evaluation indicate that this transformation is feasible, although it does not achieve significant improvements in terms of adaptation quality without fine-tuning the default search parameters. However, integration into state-of-the-art planning frameworks builds the basis for using a variety of additional features and updates, enhancing the modeling and configuration process.},
keywords = {Adaptive Workflow Management, Constrained Optimization Planning, Genetic Algorithms, Process-Oriented Case-Based Reasoning, Rule-Based Adaptation},
pubstate = {published},
tppubtype = {inproceedings}
}
The application of adaptation knowledge still poses a major challenge in modern Case-Based Reasoning (CBR) systems. This is especially the case in the subdomain of Process-Oriented Case-Based Reasoning (POCBR), where cases represent procedural experimental knowledge. Current adaptation methods in this field make use of proprietary local search techniques to apply adaptation knowledge, which is inefficient and does not allow exploitation of advanced search and optimization techniques. Therefore, this work presents an approach to transform rule-based adaptation into a planning problem that is solvable with both Constrained Optimization Planning (COP) and Genetic Algorithms (GA). The results of an experimental evaluation indicate that this transformation is feasible, although it does not achieve significant improvements in terms of adaptation quality without fine-tuning the default search parameters. However, integration into state-of-the-art planning frameworks builds the basis for using a variety of additional features and updates, enhancing the modeling and configuration process.
2024
Malburg, Lukas; Hotz, Maxim; Bergmann, Ralph
Improving Complex Adaptations in Process-Oriented Case-Based Reasoning by Applying Rule-Based Adaptation Proceedings Article
In: Case-Based Reasoning Research and Development - 32nd International Conference, ICCBR 2024, Merida, Mexico, July 1-4, 2024, Proceedings, pp. 50–66, Springer., 2024.
Abstract | Links | BibTeX | Tags: {Process-Oriented Case-Based Reasoning, Adaptation Operators}, Adaptive Workflow Management, Drools Rule Engine, Rule-Based Adaptation
@inproceedings{Malburg.2024_AdaptationRulesInPOCBR,
title = {Improving Complex Adaptations in Process-Oriented Case-Based Reasoning by Applying Rule-Based Adaptation},
author = {Lukas Malburg and Maxim Hotz and Ralph Bergmann},
url = {https://www.wi2.uni-trier.de/shared/publications/2024_ICCBR_RuleBasedAdaptation_MalburgEtAl.pdf},
doi = {10.1007/978-3-031-63646-2_4},
year = {2024},
date = {2024-01-01},
booktitle = {Case-Based Reasoning Research and Development - 32nd International Conference, ICCBR 2024, Merida, Mexico, July 1-4, 2024, Proceedings},
volume = {14775},
pages = {50–66},
publisher = {Springer.},
series = {Lecture Notes in Computer Science},
abstract = {Adaptation is a complex and error-prone task in Case-Based Reasoning (CBR), including the adaptation knowledge acquisition and modeling efforts required for performing adaptations. This is also evident for the subfield of Process-Oriented Case-Based Reasoning (POCBR) in which cases represent procedural experiential knowledge, making creation and maintaining adaptation knowledge even for domain experts exceedingly challenging. Current adaptation methods in POCBR address the adaptation knowledge bottleneck by learning adaptation knowledge based on cases in the case base. However, these approaches are based on proprietary representation formats, resulting in low usability and maintainability. Therefore, we present an approach of using adaptation rules and rule engines for complex adaptations in POCBR in this paper. The results of an experimental evaluation indicate that the rule-based adaptation approach leads to significantly better results during runtime than an already available POCBR adaptation method.},
keywords = {{Process-Oriented Case-Based Reasoning, Adaptation Operators}, Adaptive Workflow Management, Drools Rule Engine, Rule-Based Adaptation},
pubstate = {published},
tppubtype = {inproceedings}
}
Adaptation is a complex and error-prone task in Case-Based Reasoning (CBR), including the adaptation knowledge acquisition and modeling efforts required for performing adaptations. This is also evident for the subfield of Process-Oriented Case-Based Reasoning (POCBR) in which cases represent procedural experiential knowledge, making creation and maintaining adaptation knowledge even for domain experts exceedingly challenging. Current adaptation methods in POCBR address the adaptation knowledge bottleneck by learning adaptation knowledge based on cases in the case base. However, these approaches are based on proprietary representation formats, resulting in low usability and maintainability. Therefore, we present an approach of using adaptation rules and rule engines for complex adaptations in POCBR in this paper. The results of an experimental evaluation indicate that the rule-based adaptation approach leads to significantly better results during runtime than an already available POCBR adaptation method.