Propositional Measure Logic
Fuente:
arXiv
Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916747527847936 |
|---|---|
| author | Aragão, Francisco |
| author_facet | Aragão, Francisco |
| contents | We present a propositional logic with fundamental probabilistic semantics, in which each formula is given a real measure in the interval $[0,1]$ that represents its degree of truth. This semantics replaces the binarity of classical logic, while preserving its deductive structure. We demonstrate the soundness theorem, establishing that the proposed system is sound and suitable for reasoning under uncertainty. We discuss potential applications and avenues for future extensions of the theory. We apply probabilistic logic to a still refractory problem in Bayesian Networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_14693 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Propositional Measure Logic Aragão, Francisco Logic in Computer Science Artificial Intelligence Primary: 03B48, Secondary: 68T27, 60A99, 68T37 We present a propositional logic with fundamental probabilistic semantics, in which each formula is given a real measure in the interval $[0,1]$ that represents its degree of truth. This semantics replaces the binarity of classical logic, while preserving its deductive structure. We demonstrate the soundness theorem, establishing that the proposed system is sound and suitable for reasoning under uncertainty. We discuss potential applications and avenues for future extensions of the theory. We apply probabilistic logic to a still refractory problem in Bayesian Networks. |
| title | Propositional Measure Logic |
| topic | Logic in Computer Science Artificial Intelligence Primary: 03B48, Secondary: 68T27, 60A99, 68T37 |
| url | https://arxiv.org/abs/2505.14693 |