Alle Publikationen
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2019
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(2019): On Proactive, Transparent, and Verifiable Ethical Reasoning for Robots. In: Proceedings of the IEEE 107 (3), S. 541-561. DOI: 10.1109/JPROC.2019.2898267
DOI: https://doi.org/10.1109/JPROC.2019.2898267 Abstract: Previous work on ethical machine reasoning has largely been theoretical, and where such systems have been implemented, it has, in general, been only initial proofs of principle. Here, we address the question of desirable attributes for such systems to improve their real world utility, and how controllers with these attributes might be implemented. We propose that ethically critical machine reasoning should be proactive, transparent, and verifiable. We describe an architecture where the ethical reasoning is handled by a separate layer, augmenting a typical layered control architecture, ethically moderating the robot actions. It makes use of a simulation-based internal model and supports proactive, transparent, and verifiable ethical reasoning. To do so, the reasoning component of the ethical layer uses our Python-based belief-desire-intention (BDI) implementation. The declarative logic structure of BDI facilitates both transparency, through logging of the reasoning cycle, and formal verification methods. To prove the principles of our approach, we use a case study implementation to experimentally demonstrate its operation. Importantly, it is the first such robot controller where the ethical machine reasoning has been formally verified.
Keywords: BDI implementation, belief desire intention implementation, control engineering computing, Design methodology, ethical machine reasoning, ethical reasoning, Ethics, formal verification, ieee xplore, intelligent robots, layered control architecture, learning (artificial intelligence), machine learning, Moral & Ethik, Predictive models, Python, robot controller, robot programming, Robots, safety, simulation-based internal model, Social implications of technology, software architecture, transparency -
(2019) : Computational Tools for Human-Robot Interaction Design: 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI): Daegu, Korea: IEEE, S. 733-735
DOI: https://doi.org/10.1109/HRI.2019.8673221 Abstract: Robots must exercise socially appropriate behavior when interacting with humans. How can we assist interaction designers to embed socially appropriate and avoid socially inappropriate behavior within human-robot interactions? We propose a multi-faceted interaction-design approach that intersects human-robot interaction and formal methods to help us achieve this goal. At the lowest level, designers create interactions from scratch and receive feedback from formal verification, while higher levels involve automated synthesis and repair of designs. In this extended abstract, we discuss past, present, and future work within each level of our design approach.
Keywords: Angemessen(heit) (von Technik), automated synthesis, computational tools, Design methodology, Electric breakdown, Formal Methods, formal verification, human-robot interaction, human-robot interaction design, ieee xplore, Interaction Design, interaction designers, Maintenance engineering, multifaceted interaction-design approach, Programming, Robots, socially appropriate behavior, Task Analysis 2018
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(2018) : Towards Self-Explaining Digital Systems: A Design Methodology for the Next Generation: 2018 IEEE 3rd International Verification and Security Workshop (IVSW): Costa Brava, Spain: IEEE, S. 1-6
DOI: https://doi.org/10.1109/IVSW.2018.8494900 Abstract: As digital systems get ever more complex, their behaviour may at times appear unfathomable. Users will only be prepared to accept this if they are convinced that the system does indeed work correctly. Thus, we argue the need for self-explaining systems: systems that are able to explain their behaviour, and the reasons for it. In this paper, we propose first steps towards a design methodology for such systems, and argue that beyond user acceptance, self-explanation also has other applications such as self-verification and reconfiguration. We propose a conceptual framework for self-explaining systems, discuss how to achieve completeness, and consider implementation aspects.
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