Abstract
Machine ethics is an uprising sub-field of artificial intelligence fueled by the interest and concerns about the deployment of automated agents in our everyday life. As these agents gain independence from human intervention and make decisions with possible impact on human welfare, real concerns are rising across domains.Due to those reasons, various approaches have been proposed to imbue automated agents with ethical considerations. Several research currents have developed models stemming from psychology and philosophy in an effort to adapt decision-making algorithms to consider ethical values so that the impact of agents on people is bounded and guided by these notions.Most of these approaches consist of either reasoning and applying a set of well-known ethical restrictions, also known as principles (top-down), or inferring them based on carefully crafted datasets through learning algorithms (bottom-up).In this thesis, we look at the problem of implementing these ethical principles in the context of tasks involving sequences of interdependent decisions, i.e: automated planning. We show how certain notions can be modeled using preference-based frameworks, as in top-down approaches, and how these preferences can be inferred from a corpus of data like bottom-up methodologies, to develop a hybrid approach that can be applied to planning problems. An implementation for each facet of our approach is provided in order to test our ideas in practical scenarios.