Marc Serramia

Norms have been widely enacted in human and agent societies to regulate individuals’ actions. However, although legislators may have ethics in mind when establishing norms, moral values are only sometimes explicitly considered. This paper advances the state of the art by providing a method for selecting the norms to enact within a society that best aligns with the moral values of such a society. Our approach to aligning norms and values is grounded in the ethics literature. Specifically, from the literature’s study of the relations between norms, actions, and values, we formally define how actions and values relate through the so-called value judgment function and how norms and values relate through the so-called norm promotion function. We show that both functions provide the means to compute value alignment for a set of norms. Moreover, we detail how to cast our decision-making problem as an optimisation problem: finding the norms that maximise value alignment. We also show how to solve our problem using off-the-shelf optimisation tools. Finally, we illustrate our approach with a specific case study on the European Value Study.

AI research is being challenged with ensuring that autonomous agents learn to behave ethically, namely in alignment with moral values. Here, we propose a novel way of tackling the value alignment problem as a two-step process. The first step consists on formalising moral values and value aligned behaviour based on philosophical foundations. Our formalisation is compatible with the framework of (Multi-Objective) Reinforcement Learning, to ease the handling of an agent’s individual and ethical objectives. The second step consists in designing an environment wherein an agent learns to behave ethically while pursuing its individual objective. We leverage on our theoretical results to introduce an algorithm that automates our two-step approach. In the cases where value-aligned behaviour is possible, our algorithm produces a learning environment for the agent wherein it will learn a value-aligned behaviour.

Decision makers can often be confronted with the need to select a subset of objects from a set of candidate objects by just counting on preferences regarding the objects’ features. Here we formalise this problem as the dominant set selection problem. Solving this problem amounts to finding the preferences over all possible sets of objects. We accomplish so by: (i) grounding the preferences over features to preferences over the objects themselves; and (ii) lifting these preferences to preferences over all possible sets of objects. This is achieved by combining lex-cel –a method from the literature—with our novel anti-lex-cel method, which we formally (and thoroughly) study. Furthermore, we provide a binary integer program encoding to solve the problem. Finally, we illustrate our overall approach by applying it to the selection of value-aligned norm systems.