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On proactive, transparent and verifiable ethical reasoning for robots

Bremner, Paul; Dennis, Louise A.; Fisher, Michael; Winfield, Alan F.


Louise A. Dennis

Michael Fisher


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 Beliefs, Desires, Intentions (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.

Journal Article Type Article
Publication Date Mar 1, 2019
Journal Proceedings of the IEEE
Print ISSN 0018-9219
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 107
Issue 3
Pages 541-561
APA6 Citation Bremner, P., Dennis, L. A., Fisher, M., & Winfield, A. F. (2019). On proactive, transparent and verifiable ethical reasoning for robots. Proceedings of the IEEE, 107(3), 541-561.
Keywords machine ethics, transparency, verifiability, ethical robot
Publisher URL
Additional Information Additional Information : © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.


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