Value Engineering for Autonomous Agents
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Abstract (arXiv)
Machine Ethics (ME) is concerned with the design of Artificial Moral Agents (AMAs), i.e. autonomous agents capable of reasoning and behaving according to moral values. Previous approaches have treated values as labels associated with some actions or states of the world, rather than as integral components of agent reasoning. It is also common to disregard that a value-guided agent operates alongside other value-guided agents in an environment governed by norms, thus omitting the social dimension of AMAs. In this blue sky paper, we propose a new AMA paradigm grounded in moral and social psychology, where values are instilled into agents as context-dependent goals. These goals intricately connect values at individual levels to norms at a collective level by evaluating the outcomes most incentivized by the norms in place. We argue that this type of normative reasoning, where agents are endowed with an understanding of norms' moral implications, leads to value-awareness in autonomous agents. Additionally, this capability paves the way for agents to align the norms enforced in their societies with respect to the human values instilled in them, by complementing the value-based reasoning on norms with agreement mechanisms to help agents collectively agree on the best set of norms that suit their human values. Overall, our agent model goes beyond the treatment of values as inert labels by connecting them to normative reasoning and to the social functionalities needed to integrate value-aware agents into our modern hybrid human-computer societies.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 3 of its 33 references that are in the map. First posted on arXiv 17 Feb 2023.
Builds on
3 of 33 references.
References · 3
Built on by
Citing papers per year: 2024: 1; 2025: 1; 2026: 2.
4 papers.
- 2025Learning the Value Systems of Societies from Preferencescited by 2
- 2024Algorithms for learning value-aligned policies considering admissibility relaxationcited by 1
- 2026Learning the Value Systems of Agents with Preference-based and Inverse Reinforcement Learningcited by 1
- 2026Building Socio-Affective Artificial Intelligence for Interactive Multi-Agent Simulationscited by 0
- 2026Building Socio-Affective Artificial Intelligence for Interactive Multi-Agent Simulations22 Sep 2026
- 2026Learning the Value Systems of Agents with Preference-based and Inverse Reinforcement Learning4 Feb 2026
- 2025Learning the Value Systems of Societies from Preferences28 Jul 2025
- 2024Algorithms for learning value-aligned policies considering admissibility relaxation7 Jun 2024