Record link: 
http://hdl.handle.net/20.500.12708/220855
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Title: 
OFTEN-DEEPRL: On-the-Fly Teaching of Ethical Norms to Deep Reinforcement Learning Agents
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Citation: 
Lopez-Miguel, I. D., Adam, S. P., Bartocci, E., Eiter, T., & Tappler, M. (2025). OFTEN-DEEPRL: On-the-Fly Teaching of Ethical Norms to Deep Reinforcement Learning Agents. In ECAI 2025 : 28th European Conference on Artificial Intelligence, 25-30 October 2025, Bologna, Italy : Including 14th Conference on Prestigious Applications of Intelligent Systems (PAIS 2025) (pp. 3315–3322). https://doi.org/10.3233/FAIA251200
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Publisher DOI: 
10.3233/FAIA251200
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Publication Type: 
Inproceedings - Full-Paper Contribution
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Language: 
English
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Authors: 
Lopez-Miguel, Ignacio D. 
Adam, Sebastian Philipp 
Bartocci, Ezio 
Eiter, Thomas 
Tappler, Martin 
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Organisational Unit: 
E191-01 - Forschungsbereich Cyber-Physical Systems 
E192-03 - Forschungsbereich Knowledge Based Systems 
E056-13 - Fachbereich LogiCS 
E056-17 - Fachbereich Trustworthy Autonomous Cyber-Physical Systems 
E056-26 - Fachbereich Automated Reasoning 
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Published in: 
ECAI 2025 : 28th European Conference on Artificial Intelligence, 25-30 October 2025, Bologna, Italy : Including 14th Conference on Prestigious Applications of Intelligent Systems (PAIS 2025) 
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ISBN: 
9781643686318
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Volume: 
413
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Date (published): 
2025
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Event name: 
28th European Conference on Artificial Intelligence (ECAI 2025) 
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Event date: 
25-Oct-2025 - 30-Oct-2025
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Event place: 
Bologna, Italy
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Number of Pages: 
8
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Peer reviewed: 
Yes
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Keywords: 
ethical reinforcement learning; norm-guided training; deep reinforcement learning; AI agents; answer set programming; logic-based planning; policy adaptation; safe autonomous systems
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Project title: 
Training and Guiding AI Agents with Ethical Rules: ICT22-023 (WWTF Wiener Wissenschafts-, Forschu und Technologiefonds) 
Logics for Computer Science Program at TU Wien: 101034440 (European Commission) 
Bilateral Artificial Intelligence: COE 12 (FWF - Österr. Wissenschaftsfonds) 
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Research Areas: 
Logic and Computation: 50%
Computer Engineering and Software-Intensive Systems: 50%
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Science Branch: 
1010 - Mathematik: 100%
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Appears in Collections:
Conference Paper

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