Piecemeal Knowledge Acquisition for Computational Normative Reasoning

Abstract

We present a hybrid approach to knowledge acquisition and representation for machine ethics---or more generally, computational normative reasoning. Building on recent research in artificial intelligence and law, our approach is modeled on the familiar practice of decision-making under precedential constraint in the common law. We first provide a formal characterization of this practice, showing how a body of normative information can be constructed in a way that is piecemeal, distributed, and responsive to particular circumstances. We then discuss two possible applications: first, a robot childminder, and second, moral judgment in a bioethical domain.