Embracing Background Knowledge in the Analysis of Actual Causality: An Answer Set Programming Approach
This work addresses the challenge of incorporating background knowledge in causality analysis for researchers in AI and logic, but it appears incremental as it builds on existing benchmark examples without claiming broad new applications.
The paper tackled the problem of formalizing causal knowledge for analyzing actual causality by introducing a rich knowledge representation language, and applied it to accurately formalize common benchmark examples from the literature.
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.