Embracing Background Knowledge in the Analysis of Actual Causality: An Answer Set Programming Approach

Abstract

This paper presents a rich knowledge representation language aimed at formalizing causal knowledge. This language is used for accurately and directly formalizing common benchmark examples from the literature of actual causality. A definition of cause is presented and used to analyze the actual causes of changes with respect to sequences of actions representing those examples.

Under consideration for publication in Theory and Practice of Logic Programming