Publikationen

Hier finden Sie die Publikationen der Forschungsgruppe AI-Systems Engineering. Die Publikationen der am InES assoziierten Wissenschaftlerinnen und Wissenschaftler entnehmen Sie bitte den jeweiligen Webseiten der assoziierten Lehrstühle:

Publikationen der Forschungsgruppe AI-Systems Engineering

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Marton, S., Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2024). GradTree: Learning axis-aligned decision trees with gradient descent. In , Proceedings of the 38th AAAI Conference on Artificial Intelligence (S. 14323-14331). , AAAI Press: Washington, DC.
Brinkmann, J., Swoboda, P. und Bartelt, C. (2023). A multidimensional analysis of social biases in vision transformers. In , Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (S. 4914-4923). , IEEE: Paris, France.
Cohausz, L., Tschalzev, A., Bartelt, C. und Stuckenschmidt, H. (2023). Investigating the importance of demographic features for EDM-predictions. In , Proceedings of the 16th International Conference on Educational Data Mining (S. 125–136). , International Educational Data Mining Society: Bengaluru, India.
He, Y., Schreckenberger, C., Stuckenschmidt, H. und Wu, X. (2023). Towards utilitarian online learning – A review of online algorithms in open feature space. In , Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (S. 6647-6655). , International Joint Conferences on Artificial Intelligence Organization: Macao SAR.
Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2023). Outlying aspect mining via sum-product networks. In , Advances in knowledge discovery and data mining: 27th Pacific-Asia Conference on knowledge discovery and data mining, PAKDD 2023, Osaka, Japan, May 25–28, 2023 : proceedings. Part I (S. 27–38). Lecture Notes in Computer Science, Springer: Berlin [u.a.].
Schreckenberger, C., He, Y., Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2023). Online random feature forests for learning in varying feature spaces. In , Proceedings of the 37th AAAI Conference on Artificial Intelligence. Vol. 4 (S. 4587-4595). , AAAI Press: Washington, DC.
Wilken, N., Cohausz, L., Bartelt, C. und Stuckenschmidt, H. (2023). Planning landmark based goal recognition revisited: Does using initial state landmarks make sense? In , KI 2023: Advances in Artificial Intelligence : 46th Conference on AI, Berlin, Germany, September 26–29, 20023, proceedings (S. 231–244). Lecture Notes in Computer Science, Springer: Berlin [u.a.].
Cohausz, L., Wilken, N. und Stuckenschmidt, H. (2022). Plan-similarity based heuristics for goal recognition. In , 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) : PerCom Workshops 2022 (S. 316–321). , IEEE: Pisa.
Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2022). Exchangeability-aware sum-product networks. In , Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, Vienna, 23–29 July 2022 (S. 4864-4870). , International Joint Conferences on Artificial Intelligence Organization: Wien.
Oesterle, M., Bartelt, C., Lüdtke, S. und Stuckenschmidt, H. (2022). Self-learning governance of black-box multi-agent systems. In , Coordination, Organizations, Institutions, Norms, and Ethics for Governance of Multi-Agent Systems XV : International Workshop, COINE 2022, virtual event, May 9, 2022, revised selected papers (S. 73–91). Lecture Notes in Computer Science, Springer: Berlin [u.a.].
Schreckenberger, C., Bartelt, C. und Stuckenschmidt, H. (2022). Dynamic forest for learning from data streams with varying feature spaces. In , Cooperative information systems : 28th International Conference, CoopIS 2022, Bozen-Bolzano, Italy, October 4–7, 2022, Proceedings (S. 95–111). Lecture Notes in Computer Science, Springer: Berlin [u.a.].
Wilken, N., Cohausz, L., Schaum, J., Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2022). Leveraging planning landmarks for hybrid online goal recognition. In , International Conference on Automated Planning and Scheduling ICAPS (2022) : June 13–17, 2022, virtual (S. ). , CEUR Workshop Proceedings: Aachen.
Burzlaff, F. und Bartelt, C. (2021). Knowledge-driven architecture composition: Assisting the system integrator to reuse integration knowledge. In , ICWE 2021 : 21st International Conference on Web Engineering, Biarritz, France, May 18–21, 2021 ; proceedings (S. 305–319). Lecture Notes in Computer Science, Springer: Berlin [u.a.].
Hoffmann, L., Bartelt, C. und Stuckenschmidt, H. (2021). Knowledge injection via ML-based initialization of neural networks. In , Proceedings of the CIKM 2021 Workshops (CIKMW 2021) co-located with 30th ACM International Conference on Information and Knowledge Management (CIKM 2021) : Gold Coast, Queensland, Australia, November 1–5,2021 (S. 1–6). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
Kolthoff, K., Bartelt, C. und Ponzetto, S. P. (2021). Automated Retrieval of Graphical User Interface Prototypes from Natural Language Requirements. In , NLDB 2021 : 26th International Conference on Natural Language & Information Systems, Saarbrücken, Germany, June 23–25, 2021 ; proceedings (S. 376–384). Lecture Notes in Computer Science, Springer: Berlin [u.a.].
Metzger, N., Hoffmann, L., Bartelt, C., Stuckenschmidt, H., Wommer, M. und Bescos del Castillo, M. B. (2021). Towards trace-graphs for data-driven test case mining in the domain of automated driving. In , Third IEEE International Conference on Artificial Intelligence Testing: AITest 2021 : proceedings : 23–26 August 2021, online event (S. 41–48). , IEEE: Piscataway, NJ.
Nolte, F., Wilken, N. und Bartelt, C. (2021). Rendezvous delivery: Utilizing autonomous electric vehicles to improve the efficiency of last mile parcel delivery in urban areas. In , 2021 IEEE PerCom Workshops : PerAwareCity 2021, 6th IEEE Workshop on Pervasive Context-Aware Smart Cities and Intelligent Transportation Systems, March 22–26, 2021 in Kassel, Germany (S. 148–153). , IEEE Computer Society: Piscataway, NJ.
Pernpeintner, M. (2021). Self-learning governance of competitive multi-agent systems. In , Organic Computing : Doctoral Dissertation Colloquium 2020 (S. 47–63). Intelligent Embedded Systems, Kassel University Press: Kassel.
Pernpeintner, M. (2021). Toward a self-learning governance loop for competitive multi-attribute MAS. In , AAMAS '21: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems : Richland, SC, Virtual Event United Kingdom, May, 2021 (S. 1619-1621). , International Foundation for Autonomous Agents and Multiagent Systems: Richland, SC.
Pernpeintner, M., Bartelt, C. und Stuckenschmidt, H. (2021). Governing black-box agents in competitive multi-agent systems. In , Multi-Agent Systems : 18th European Conference, EUMAS 2021, virtual event, June 28–29, 2021, revised selected papers (S. 19–36). Lecture Notes in Computer Science, Springer: Berlin [u.a.].
Wilken, N. und Stuckenschmidt, H. (2021). Combining symbolic and statistical knowledge for goal recognition in smart home environments. In , 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) (S. 26–31). , IEEE Computer Society: Piscataway, NJ.
Wilken, N., Stuckenschmidt, H. und Bartelt, C. (2021). Combining symbolic and data-driven methods for goal recognition. In , 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) (S. 428–429). , IEEE Computer Society: Piscataway, NJ.
Burzlaff, F., Bongarth, B., Grottker, S., Hammen, J. und Bartelt, C. (2020). MergePoint: A graphical web-app for merging HTTP-endpoints and IoT-platform models. In , 53rd Hawaii International Conference on System Sciences, HICSS 2020 : Maui, Hawaii, USA, January 7–10, 2020 (S. 1–10). Proceedings of the 53rd Hawaii International Conference on System Sciences, ScolarSpace: Honolulu, HI.
Kolthoff, K. (2020). Automatic generation of graphical user interface prototypes from unrestricted natural language requirements. In , 2019 34th IEEE/ACM International Conference on Automated Software Engineering : 10–15 November 2019 : San Diego, California : proceedings (S. 1234-1237). , IEEE: Piscataway, NJ.
Kolthoff, K., Bartelt, C. und Ponzetto, S. P. (2020). GUI2WiRe: Rapid wireframing with a mined and large-scale GUI repository using natural language requirements. In , ASE 2020 : 35th IEEE/ACM International Conference on Automated Software Engineering : Mon 21 – Fri 25 September 2020, Melbourne, Australia : Tool Demonstrations (S. 1297-1301). , ACM: New York, NY.
Pernpeintner, M. (2020). Achieving emergent governance in competitive multi-agent systems. In , AAMAS '20 : Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, Auckland, Nea Zealand, May 2020 (S. 2204-2206). , ACM Digital Library: New York, NY.
Schindler, C. (2020). Towards transparency-encouraging partial software disclosure to enable trust in data usage. In , ASE 2020 : 35th IEEE/ACM International Conference on Automated Software Engineering : Mon 21 – Fri 25 September 2020, Melbourne, Australia : Doctoral Symposium (S. 1–3). , Association for Computing Machinery: New York, NY.
Schreckenberger, C., Bartelt, C. und Stuckenschmidt, H. (2020). Robust decision tree induction from unreliable data sources. In , STAIRS 2020 : Proceedings of the 9th European Starting AI Researchers' Symposium 2020 co-located with 24th European Conference on Artificial Intelligence (ECAI 2020) Santiago Compostela, Spain, August, 2020 (S. Paper 6, 1–8). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
Schreckenberger, C., Glockner, T., Stuckenschmidt, H. und Bartelt, C. (2020). Restructuring of Hoeffding trees for Trapezoidal Data Streams. In , 20th IEEE International Conference on Data Mining Workshops : 17–20 November 2020, Virtual Conference : Proceedings (S. 416–423). , IEEE: Los Alamitos, CA [u.a.].
Wieber, N. (2020). Automated generation of client-specific backends utilizing existing microservices and architectural knowledge. In , ASE 2020 : 35th IEEE/ACM International Conference on Automated Software Engineering : Mon 21 – Fri 25 September 2020, Melbourne, Australia : Doctoral Symposium (S. 1–3). , Association for Computing Machinery: New York, NY.
Burzlaff, F., Ackel, M. und Bartelt, C. (2019). A mapping language for IoT device descriptions. In , 2019 IEEE 43rd Annual Computer Software and Applications Conference : 15–19 July 2019, Milwaukee, Wisconsin : proceedings (S. 115–120). , IEEE Computer Society: Piscataway, NJ.
Burzlaff, F. und Bartelt, C. (2019). A conceptual architecture for enabling future self-adaptive service systems. In , 52nd Hawaii International Conference on System Sciences, HICSS 2019, Grand Wailea, Maui, Hawaii, USA, January 8–11, 2019 (S. 1–10). , AISeL: Atlanta, GA.
Pernpeintner, M. (2019). Collaboration as an emergent property of self-organizing software systems. In , 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems : FAS*W 2019 : proceedings (S. 231–233). , IEEE Computer Society Press: Piscataway, NJ.
Schreckenberger, C., Bartelt, C. und Stuckenschmidt, H. (2019). Enhancing a crowd-based delivery network with mobility predictions. In , PredictGIS'19 : Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility : Chicago, IL, USA, November 05, 2019 (S. 66–75). Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Prediction of Human Mobility, ACM: New York, NY.
Schreckenberger, C., Bartelt, C. und Stuckenschmidt, H. (2019). iDropout: Leveraging deep taylor decomposition for the robustness of deep neural networks. In , On the Move to Meaningful Internet Systems: OTM 2019 Conferences : Confederated International Conferences: CoopIS, ODBASE, C&TC 2019, Rhodes, Greece, October 21–25, 2019, Proceedings (S. 113–126). Lecture Notes in Computer Science, Springer: Berlin [u.a.].
Schreckenberger, C., Beckmann, S. und Bartelt, C. (2019). Next place prediction: A systematic literature review. In , PredictGIS 2018 : Proceedings of the 2nd ACM SIGSPATIAL Workshop on Prediction of Human Mobility : ACM GIS 2018 Conference: November 6 – November 9, 2018, Seattle, Washington (S. 37–45). Proceedings of the 2Nd ACM SIGSPATIAL Workshop on Prediction of Human Mobility, ACM: New York, NY.
Burzlaff, F. und Bartelt, C. (2018). I4.0-device integration: A qualitative analysis of methods and technologies utilized by system integrators: Implications for enginering future industrial internet of things system. In , 2018 IEEE 15th International Conference on Software Architecture companion : ICSA-C 2018 : proceedings : 30 April-4 May 2018, Seattle, Washington (S. 27–34). , IEEE: Piscataway, NJ.
Burzlaff, F., Bartelt, C. und Adler, L. (2018). Towards automating service matching for manufacturing systems: Exemplifying knowledge-driven architecture composition. In , 51st CIRP Conference on Manufacturing Systems (CIRP CMS 2018), Stockholm, Sweden, 16–18 May 2018 (S. 707–713). Procedia CIRP, Elsevier ; Curran: Amsterdam ; Red Hook, NY.
Burzlaff, F., Bartelt, C. und Jacobs, S. (2018). Executing model-based software development for embedded I4.0 devices properly. In , MOD-WS 2018 : Joint Proceedings of the Workshops at Modellierung 2018 co-located with Modellierung 2018, Braunschweig, Germany, February 21, 2018 (S. 35–46). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
Burzlaff, F., Bartelt, C. und Stuckenschmidt, H. (2018). Next steps in knowledge-driven architecture composition. In , LWDA 2018 : Proceedings of the Conference „Lernen, Wissen, Daten, Analysen“ Mannheim, Germany, August 22–24, 2018 (S. 78–83). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
Achichi, M., Cheatham, M., Dragisic, Z., Euzenat, J., Faria, D., Ferrara, A., Flouris, G., Fundulaki, I., Harrow, I., Ivanova, V., Jiménez-Ruiz, E., Kolthoff, K., Kuss, E., Lambrix, P., Leopold, H., Li, H., Meilicke, C., Mohammadi, M., Montanelli, S., Pesquita, C., Saveta, T., Shvaiko, P., Splendiani, A., Stuckenschmidt, H., Thiéblin, E., Todorov, K., Trojahn, C. und Zamazal, O. (2017). Results of the Ontology Alignment Evaluation Initiative 2017. In , OM 2017 : Proceedings of the 12th International Workshop on Ontology Matching co-located with the 16th International Semantic Web Conference (ISWC 2017), Vienna, Austria, October 21, 2017 (S. 61–113). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
Burzlaff, F. (2017). Knowledge-driven architecture composition. In , 47. Jahrestagung der Gesellschaft für Informatik, Informatik 2017, Chemnitz, Germany, September 25–29, 2017 (S. 2365-2370). Lecture Notes in Informatics, Gesellschaft für Informatik e.V.: Bonn.
Burzlaff, F. und Bartelt, C. (2017). Knowledge-driven architecture composition: Case-based formalization of integration knowledge to enable automated component coupling. In , ICSA 2017 : 2017 IEEE International Conference on Software Architecture : side track proceedings : proceedings : 3–7 April 2017, Gothenburg, Sweden (S. 108–111). , IEEE: Piscataway, NJ.
Atkinson, C., Gerbig, R. und Metzger, N. (2015). On the execution of deep models. In , EXE 2015 : Proceedings of the 1st International Workshop on Executable Modeling co-located with ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS 2015) Ottawa, Canada, September 27, 2015 (S. 28–33). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
De Loera, J. A., Margulies, S., Pernpeintner, M., Riedl, E., Rolnick, D., Spencer, G., Stasi, D. und Swenson, J. (2015). Graph coloring ideals : Nullstellensatz certificates, Gröbner bases for chordal graphs, and hardness of Gröbner bases. In , ISSAC '15 : proceedings of the 2015 ACM International Symposium on Symbolic and Algebraic Computation : July 6–9, 2015, Bath, United Kingdom (S. 133–140). , ACM: New York, NY.
Kolthoff, K. und Dutta, A. (2015). Semantic relation composition in large scale knowledge bases. In , Linked Data for Information Extraction : Proceedings of the Third International Workshop on Linked Data for Information Extraction (LD4IE2015) co-loc. with the 14th International Semantic Web Conference (ISWC 2015) ; Bethlehem, PA, USA, Oct. 12, 2015 (S. 34–47). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.

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Marton, S., Lüdtke, S., Bartelt, C., Tschalzev, A. und Stuckenschmidt, H. (2024). Explaining neural networks without access to training data. Machine Learning, 1–20.
Rink, J., Tollens, F., Tschalzev, A., Bartelt, C., Heinzl, A., Hoffmann, J., Schoenberg, S. O., Marzina, A., Sandikci, V., Wiegand, C., Hoyer, C. und Szabo, K. (2024). Establishing an MSU service in a medium-sized German urban area — clinical and economic considerations. Frontiers in Neurolgy, 15, 1–9.
Kolthoff, K., Bartelt, C. und Ponzetto, S. P. (2023). Correction to: Data-driven prototyping via natural- language-based GUI retrieval. Automated Software Engineering, 30, 1–2.
Kolthoff, K., Bartelt, C. und Ponzetto, S. P. (2023). Data-driven prototyping via natural-language-based GUI retrieval. Automated Software Engineering, 30, 1–34.
Umlauft, J., Johnson, C. W., Roux, P., Trugman, D. T., Lecointre, A., Walpersdorf, A., Nanni, U., Gimbert, F., Rouet-Leduc, B., Hulbert, C., Lüdtke, S., Marton, S. und Johnson, P. A. (2023). Mapping glacier basal sliding applying machine learning. Journal of geophysical research : JGR. F, Earth surface, 128, 1–20.
Burzlaff, F., Wilken, N., Bartelt, C. und Stuckenschmidt, H. (2022). Semantic interoperability methods for smart service systems: A survey. IEEE Transactions on Engineering Management : EM, 69, 4052-4066.
Marton, S., Lüdtke, S. und Bartelt, C. (2022). Explanations for neural networks by neural networks. Applied Sciences, 12, 1–14.
Niemann, F., Lüdtke, S., Bartelt, C. und Ten Hompel, M. (2022). Context-aware human activity recognition in industrial processes. Sensors, 22, 1–14.
Klingert, S., Wilken, N. und Becker, C. (2020). Sim2Win: How simulation can help data centers to benefit from controlling their power profile. Energy Efficiency, 13, 1007-1029.

Marton, S., Bartelt, C. und Stuckenschmidt, H. (2020). Machine learning for converting Black-Box models to interpretable functions. PhD Forum, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2020, Online.
Schreckenberger, C., Bartelt, C. und Stuckenschmidt, H. (2020). Tree-based learning for dynamic data streams. PhD Forum, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2020, Online.
Metzger, N. und Weil, S. (2019). DFG-Projekt : Optimierter Einsatz von OCR-Verfahren – Tesseract als Komponente im OCR-D-Workflow. Workshop der Heidelberger MAD|HD-Gruppe, Heidelberg, Germany.

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Publikationen der Forschungsgruppe AI-Systems Engineering

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