Abstract
AbstractThis 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.
Publisher
Cambridge University Press (CUP)
Subject
Artificial Intelligence,Computational Theory and Mathematics,Hardware and Architecture,Theoretical Computer Science,Software