This presentation will first recall what are the foundations of fuzzy logic and probabilistic logic. It will show the relevance of fuzzy logic for the modeling of categorization and the relevance of probabilistic logic for the modeling of causal cognition. This will lead us to the development of probabilistic fuzzy logic and to the presentation of its rules. Then, we will show how such a logical system can be implemented in programming and this will constitute a contribution to explainable AI and to the modeling of cognition.

References

Robert, S. & Brisson, J. (2016) The Klein Group, Squares of Opposition and the Explanation of Fallacies in Reasoning. Logica Universalis, Springer Birkhäuser, volume 10, issue 2-3, p. 377-392.

Evans, J. St B. T., Newstead, S. E. and Byrne, R. M. J. (1993). Human Reasoning: The Psychology of Deduction. Hove UK: Lawrence Erlbaum Associates. Chapter 2.

Stenning, K & van Lambalgen, M. (2008). Human Reasoning and Cognitive Science. Cambridge MA: MIT Press.

Robert, S., Faghihi, U., Barkaoui, Y., & Ghazzali, N. (2020). Causality in probabilistic fuzzy logic and alternative causes as fuzzy duals. In Advances in Computational Collective Intelligence: 12th International Conference, ICCCI 2020, Da Nang, Vietnam, November 30–December 3, 2020, Proceedings 12 (pp. 767-776). Springer International Publishing.

Faghihi, U., Robert, S., Poirier, P., & Barkaoui, Y. (2020, May). From association to reasoning, an alternative to pearls’ causal reasoning. In The Thirty-Third International Flairs Conference.