Specification, validation and verification of social, legal, ethical, empathetic and cultural requirements for autonomous agents
Autonomous agents are increasingly being proposed for use in healthcare, assistive care, education, and other applications governed by complex human-centric norms. To ensure compliance with these norms, the rules they induce need to be unambiguously defined, checked for consistency, and used to verify the agent. In this paper, we introduce a framework for formal specification, validation and verification of social, legal, ethical, empathetic and cultural (SLEEC) rules for autonomous agents. Our framework comprises: (i) a language for specifying SLEEC rules and rule defeaters (that is, circumstances in which a rule does not apply or an alternative form of the rule is required); (ii) a formal semantics (defined in the process algebra tock-CSP) for the language; and (iii) methods for detecting conflicts and redundancy within a set of rules, and for verifying the compliance of an autonomous agent with such rules. We show the applicability of our framework for two autonomous agents from different domains: a firefighter UAV, and an assistive-dressing robot. • A formal approach to specifying, validating and verifying normative requirements. • A domain-specific language with formal semantics for normative requirements. • End-to-end tool support for normative requirements engineering. • Case studies showing applicability to a firefighter drone and an assistive-care robot.
Authors
- Radu Călinescu (ORCID: https://orcid.org/0000-0002-2678-9260)
- Pedro Ribeiro (ORCID: https://orcid.org/0000-0003-4319-4872)
- Beverley Townsend (ORCID: https://orcid.org/0000-0002-8486-6041)
- Sinem Getir Yaman (ORCID: https://orcid.org/0000-0002-3944-6574)
- Colin Paterson (ORCID: https://orcid.org/0000-0002-6678-3752)
- Ana Cavalcanti (ORCID: https://orcid.org/0000-0002-0831-1976)
Institutions
- University of York (GB)
Publication Details
- Journal
- Journal of Systems and Software
- Published
- 2024-10-05
- DOI
- https://doi.org/10.1016/j.jss.2024.112229
- Citations
- 4
- Primary Topic
- Robot Manipulation and Learning
- Type
- article
- Field-Weighted Citation Impact
- 1.31
Funders
- UK Research and Innovation
- Royal Academy of Engineering
- Engineering and Physical Sciences Research Council