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

Published online by Cambridge University Press:  26 July 2023

MICHAEL GELFOND
Affiliation:
Department of Computer Science, Texas Tech University, Lubbock, TX, USA (e-mail: michael.gelfond@ttu.edu)
JORGE FANDINNO
Affiliation:
Department of Computer Science, University of Nebraska at Omaha, Omaha, NE, USA (e-mail: jfandinno@unomaha.edu)
EVGENII BALAI
Affiliation:
Department of Computer Science, Texas Tech University, Lubbock, TX, USA (e-mail: evgenii.balai@gmail.com)
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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.

Information

Type
Original Article
Copyright
© The Author(s), 2023. Published by Cambridge University Press
Figure 0

Fig. 1. Causal mechanisms in the background theory representing the Engineer story.

Figure 1

Fig. 2. Proofs $P_1$ and $P_2$ of $\mathit{arrived}(\mathit{dest})$ in scenario $\mathcal{S}_{eng}$ with any interpretation $\gamma$ satisfying condition ${\gamma(t_3)+\gamma(\mathit{time2fork}) > \gamma(t_4)}$. We use $n_1$ to denote the positive natural number $\gamma(t_3)+\gamma(\mathit{time2fork})$. Only $P_1$ is a tight proof.