Embracing Background Knowledge in the Analysis of Actual Causality: An Answer Set Programming Approach
Artificial Intelligence
2023-06-07 v1 Logic in Computer Science
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.
Cite
@article{arxiv.2306.03874,
title = {Embracing Background Knowledge in the Analysis of Actual Causality: An Answer Set Programming Approach},
author = {Michael Gelfond and Jorge Fandinno and Evgenii Balai},
journal= {arXiv preprint arXiv:2306.03874},
year = {2023}
}
Comments
Under consideration for publication in Theory and Practice of Logic Programming