In the present paper, we axiomatize a logic that al- lows a general approach for reasoning about probability functions, belief functions, lower probabilities and their corresponding duals. The formal setting we consider arises from combining a modal S5 necessity operator that applies to the formulas of the infinite-valued Łukasiewicz logic with the unary modality 𝑃 that de- scribes the behaviour of probability functions. The modality 𝑃 together with an S5 modality provides a language rich enough to characterise probability, belief and lower probability theories. For this logic, we provide an axiomatization and we prove that, once we restrict to suitable sublanguages, it turns out to be sound and complete with respect to belief functions and lower probability models.
A Modal Logic for Uncertainty: a Completeness Theorem / E.A. Corsi, T. Flaminio, L. Godo, H. Hosni (PROCEEDINGS OF MACHINE LEARNING RESEARCH). - In: International Symposium on Imprecise Probability: Theories and Applications / [a cura di] E. Miranda, I. Montes, E. Quaeghebeur, B. Vantaggi. - [s.l] : Proceedings of Machine Learning Research, 2023. - pp. 119-129 (( convegno ISIPTA tenutosi a Oviedo nel 2023.
A Modal Logic for Uncertainty: a Completeness Theorem
E.A. Corsi
Primo
;H. HosniUltimo
2023
Abstract
In the present paper, we axiomatize a logic that al- lows a general approach for reasoning about probability functions, belief functions, lower probabilities and their corresponding duals. The formal setting we consider arises from combining a modal S5 necessity operator that applies to the formulas of the infinite-valued Łukasiewicz logic with the unary modality 𝑃 that de- scribes the behaviour of probability functions. The modality 𝑃 together with an S5 modality provides a language rich enough to characterise probability, belief and lower probability theories. For this logic, we provide an axiomatization and we prove that, once we restrict to suitable sublanguages, it turns out to be sound and complete with respect to belief functions and lower probability models.File | Dimensione | Formato | |
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