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/lp/springer-journals/a-guided-tour-of-artificial-intelligence-research-a-glance-at-WyZlV2HRYA
- 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