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A Guided Tour of Artificial Intelligence ResearchA Glance at Causality Theories for Artificial Intelligence

A Guided Tour of Artificial Intelligence Research: A Glance at Causality Theories for Artificial... [Causality plays a key role in the understanding of the world by humans. As such, it has been considered by artificial intelligence researchers from different perspectives ranging from the use of causal links in diagnosis or in reasoning about action to the ascription of causality relations and the assessment of responsibility. In the last two decades, some formal models of causality, such as those proposed by Pearl and Halpern, have been much influential beyond the field of artificial intelligence because they account for the distinction between actual causality and spurious correlations. Yet other aspects of causality modeling are worth of interest, such as the role played by the notion of abnormality, since what we need to explain are often deviations from the normal course of things. The chapter provides a brief but extensive overview of the artificial intelligence literature dealing with causality, albeit without the ambition of giving a complete account of works by philosophers and psychologists that have influenced it.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Guided Tour of Artificial Intelligence ResearchA Glance at Causality Theories for Artificial Intelligence

Editors: Marquis, Pierre; Papini, Odile; Prade, Henri

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References (116)

Publisher
Springer International Publishing
Copyright
© Springer Nature Switzerland AG 2020
ISBN
978-3-030-06163-0
Pages
275 –305
DOI
10.1007/978-3-030-06164-7_9
Publisher site
See Chapter on Publisher Site

Abstract

[Causality plays a key role in the understanding of the world by humans. As such, it has been considered by artificial intelligence researchers from different perspectives ranging from the use of causal links in diagnosis or in reasoning about action to the ascription of causality relations and the assessment of responsibility. In the last two decades, some formal models of causality, such as those proposed by Pearl and Halpern, have been much influential beyond the field of artificial intelligence because they account for the distinction between actual causality and spurious correlations. Yet other aspects of causality modeling are worth of interest, such as the role played by the notion of abnormality, since what we need to explain are often deviations from the normal course of things. The chapter provides a brief but extensive overview of the artificial intelligence literature dealing with causality, albeit without the ambition of giving a complete account of works by philosophers and psychologists that have influenced it.]

Published: May 8, 2020

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