Explaining Actual Causation via Reasoning About Actions and Change

The study of actual causation concerns reasoning about events that have been instrumental in bringing about a particular outcome. Although the subject has long been studied in a number of fields including artificial intelligence, existing approaches have not yet reached the point where their results can be directly applied to explain causation in certain advanced scenarios, such as pin-pointing causes and responsibilities for the behavior of a complex cyber-physical system. We believe that this is due, at least in part, to a lack of distinction between the laws that govern individual states of the world and events whose occurrence cause state to evolve. In this paper, we present a novel approach to reasoning about actual causation that leverages techniques from Reasoning about Actions and Change to identify detailed causal explanations for how an outcome of interest came to be. We also present an implementation of the approach that leverages Answer Set Programming.