Embracing Background Knowledge in the Analysis of Actual Causality: an Answer Set Programming Approach.
Theory and practice of logic programming(2023)
摘要
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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关键词
answer set programming,causality,knowledge representation
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