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Erschienen in:
2020 | OriginalPaper | Buchkapitel
Causality in Probabilistic Fuzzy Logic and Alternative Causes as Fuzzy Duals
verfasst von : Serge Robert, Usef Faghihi, Youssef Barkaoui, Nadia Ghazzali
Erschienen in: Advances in Computational Collective Intelligence
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Abstract
Causal inference is an essential part of human reasoning. Computer scientists often use inferential logic and probability theories to find the causes of the events. In causal inference, researchers are interested in finding the relationship between two observable events. However, most of the time, we can only perceive the co-occurrences of events or usual successions of events. So, we must be careful in trying to establish causality between events. Also, many alternative causes or conjoint causes can be at work. So, we need good tools to deal with the complex problem of causality. In this paper, we will explore probabilistic fuzzy logic (PFL) as a sophisticated alternative to establish causal inference and fuzzy duals as tools for the expression of alternative possible causes.