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

Michael Gelfond, Jorge Fandinno, Evgenii Balai
6/6/2023
cs.AIcs.LO

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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.

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Cite this paper

@article{gelfond2023embracing,
  title  = {Embracing Background Knowledge in the Analysis of Actual Causality: An Answer Set Programming Approach},
  author = {Michael Gelfond and Jorge Fandinno and Evgenii Balai},
  year   = {2023},
  eprint = {2306.03874},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.03874v1}
}

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