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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 Open the ORCID record for MICHAEL GELFOND [Opens in a new window] ,
JORGE FANDINNO Open the ORCID record for JORGE FANDINNO [Opens in a new window]  and
EVGENII BALAI Open the ORCID record for EVGENII BALAI [Opens in a new window]
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.

Keywords

answer set programmingcausalityknowledge representation
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 23 , Issue 4: 2023 International Conference on Logic Programming , July 2023 , pp. 715 - 729
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Copyright
© The Author(s), 2023. Published by Cambridge University Press

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