학술논문

Explaining actual causation via reasoning about actions and change.
Document Type
Proceedings Paper
Author
LeBlanc, Emily (1-DREX-NDM) AMS Author Profile; Balduccini, Marcello (1-STJO-NDM) AMS Author Profile; Vennekens, Joost (B-KUL4) AMS Author Profile
Source
Logics in artificial intelligence (20190101), 231-246.
Subject
68 Computer science -- 68T Artificial intelligence
  68T30 Knowledge representation
Language
English
Abstract
Summary: ``The study of actual causation concerns reasoning about events that have been instrumental in bringing about a particular outcome. Although the subject has long been studied in a number of fields including artificial intelligence, existing approaches have not yet reached the point where their results can be directly applied to explain causation in certain advanced scenarios, such as pin-pointing causes and responsibilities for the behavior of a complex cyber-physical system. We believe that this is due, at least in part, to a lack of distinction between the laws that govern individual states of the world and events whose occurrence cause state to evolve. In this paper, we present a novel approach to reasoning about actual causation that leverages techniques from Reasoning about Actions and Change to identify detailed causal explanations for how an outcome of interest came to be. We also present an implementation of the approach that leverages Answer Set Programming.''

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