Abstract
Value alignment has emerged in recent years as a basic principle to produce beneficial and mindful Artificial Intelligence systems. It mainly states that autonomous entities should behave in a way that is aligned with our human values. In this work, we summarize a previously developed model that considers values as preferences over states of the world and defines alignment between the governing norms and the values. We provide a use-case for this framework with the Iterated Prisoner’s Dilemma model, which we use to exemplify the definitions we review. We take advantage of this use-case to introduce new concepts to be integrated with the established framework: alignment equilibrium and Pareto optimal alignment. These are inspired on the classical Nash equilibrium and Pareto optimality, but are designed to account for any value we wish to model in the system.
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Acknowledgments
This work has been supported by the AppPhil project (RecerCaixa 2017), the CIMBVAL project (funded by the Spanish government, project # TIN2017-89758-R), the EU WeNet project (H2020 FET Proactive project # 823783) and the EU TAILOR project (H2020 # 952215).
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Montes, N., Sierra, C. (2021). Value-Alignment Equilibrium in Multiagent Systems. In: Heintz, F., Milano, M., O'Sullivan, B. (eds) Trustworthy AI - Integrating Learning, Optimization and Reasoning. TAILOR 2020. Lecture Notes in Computer Science(), vol 12641. Springer, Cham. https://doi.org/10.1007/978-3-030-73959-1_17
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DOI: https://doi.org/10.1007/978-3-030-73959-1_17
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