Abstract
There have been a number of attempts to develop a formal definition of causality that accords with our intuitions about what constitutes a cause. Perhaps the best known is the “modified” definition of actual causality, HPm, due to Halpern. In this paper, we argue that HPm gives counterintuitive results for some simple causal models. We propose Dynamic Causality (DC), an alternative semantics for causal models that leads to an alternative definition of causes. DC ascribes the same causes as HPm on the examples of causal models widely discussed in the literature and ascribes intuitive causes for the kinds of causal models we consider. Moreover, we show that the complexity of determining a cause under the DC definition is lower than for the HPm definition.
Original language | English |
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Title of host publication | ECAI 2023 - 26th European Conference on Artificial Intelligence, including 12th Conference on Prestigious Applications of Intelligent Systems, PAIS 2023 - Proceedings |
Editors | Kobi Gal, Kobi Gal, Ann Nowe, Grzegorz J. Nalepa, Roy Fairstein, Roxana Radulescu |
Publisher | IOS Press |
Pages | 867-874 |
Number of pages | 8 |
Volume | 372 |
ISBN (Electronic) | 9781643684369 |
ISBN (Print) | 978-1-64368-436-9 |
DOIs | |
Publication status | Published - 28 Sept 2023 |
Publication series
Name | Frontiers in Artificial Intelligence and Applications |
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Volume | 372 |
ISSN (Print) | 0922-6389 |
Bibliographical note
Publisher Copyright:© 2023 The Authors.