Instilling moral value alignment by means of multi-objective reinforcement learning

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

On the dominant set selection problem and its application to value alignment

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

Reinforcement Learning for Value Alignment

[eng] As autonomous agents become increasingly sophisticated and we allow them to perform more complex tasks, it is of utmost importance to guarantee that they will act in alignment with human values. This problem has received in the AI literature the name of the value alignment problem. Current approaches apply reinforcement learning to align agents with values due to its recent successes at solving complex sequential decision-making problems. However, they follow an agent-centric approach by expecting that the agent applies the reinforcement learning algorithm correctly to learn an ethical behaviour, without formal guarantees that the learnt ethical behaviour will be ethical. This thesis proposes a novel environment-designer approach for solving the value alignment problem with theoretical guarantees. Our proposed environment-designer approach advances the state of the art with a process for designing ethical environments wherein it is in the agent's best interest to learn ethical behaviours. Our process specifies the ethical knowledge of a moral value in terms that can be used in a reinforcement learning context. Next, our process embeds this knowledge in the agent's learning environment to design an ethical learning environment. The resulting ethical environment incentivises the agent to learn an ethical behaviour while pursuing its own objective. We further contribute to the state of the art by providing a novel algorithm that, following our ethical environment design process, is formally guaranteed to create ethical environments. In other words, this algorithm guarantees that it is in the agent's best interest to learn value- aligned behaviours. We illustrate our algorithm by applying it in a case study environment wherein the agent is expected to learn to behave in alignment with the moral value of respect. In it, a conversational agent is in charge of doing surveys, and we expect it to ask the users questions respectfully while trying to get as much information as possible. In the designed ethical environment, results confirm our theoretical results: the agent learns an ethical behaviour while pursuing its individual objective.[cat] A mesura que els agents autònoms es tornen cada cop més sofisticats i els permetem realitzar tasques més complexes, és de la màxima importància garantir que actuaran d'acord amb els valors humans. Aquest problema ha rebut a la literatura d'IA el nom del problema d'alineació de valors. Els enfocaments actuals apliquen aprenentatge per reforç per alinear els agents amb els valors a causa dels seus èxits recents a l'hora de resoldre problemes complexos de presa de decisions seqüencials. Tanmateix, segueixen un enfocament centrat en l'agent en esperar que l'agent apliqui correctament l'algorisme d'aprenentatge de reforç per aprendre un comportament ètic, sense garanties formals que el comportament ètic après serà ètic. Aquesta tesi proposa un nou enfocament de dissenyador d'entorn per resoldre el problema d'alineació de valors amb garanties teòriques. El nostre enfocament de disseny d'entorns proposat avança l'estat de l'art amb un procés per dissenyar entorns ètics en què és del millor interès de l'agent aprendre comportaments ètics. El nostre procés especifica el coneixement ètic d'un valor moral en termes que es poden utilitzar en un context d'aprenentatge de reforç. A continuació, el nostre procés incorpora aquest coneixement a l'entorn d'aprenentatge de l'agent per dissenyar un entorn d'aprenentatge ètic. L'entorn ètic resultant incentiva l'agent a aprendre un comportament ètic mentre persegueix el seu propi objectiu. A més, contribuïm a l'estat de l'art proporcionant un algorisme nou que, seguint el nostre procés de disseny d'entorns ètics, està garantit formalment per crear entorns ètics. En altres paraules, aquest algorisme garanteix que és del millor interès de l'agent aprendre comportaments alineats amb valors. Il·lustrem el nostre algorisme aplicant-lo en un estudi de cas on s'espera que l'agent aprengui a comportar-se d'acord amb el valor moral del respecte. En ell, un agent de conversa s'encarrega de fer enquestes, i esperem que faci preguntes als usuaris amb respecte tot intentant obtenir la màxima informació possible. En l'entorn ètic dissenyat, els resultats confirmen els nostres resultats teòrics: l'agent aprèn un comportament ètic mentre persegueix el seu objectiu individual

A Qualitative Approach to Composing Value-Aligned Norm Systems Autores

Research in normative multi-agent systems has explored several approaches to compute the norm system (set of norms) required to make coordination possible. More recently, norm selection supposes an already available collection of norms from which to select a norm system to enact. A key aspect in this selection process is the consideration of moral values together with preferences among them, thus the selection follows the principle: the more preferred the values promoted by a norm system, the more preferred the norm system. Unfortunately, norm selection follows a quantitative approach despite the qualitative nature of the information available to the decision maker. In this paper we provide a novel qualitative approach to norm selection by formalising the process to infer a norm system ranking from the value preferences. We provide an encoding of this qualitative problem into a linear program and show that their solutions are equivalent.Funded by AI4EU(H2020-825619), H2020-769142, PGC2018-096212-B-C33, 19S01329- 001 by Ajuntament de Barcelona (Fundació Solidaritat UB