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Complexity of Łukasiewicz Modal Probabilistic Logics

Published: November 27, 2025 | arXiv ID: 2511.22389v1

By: Daniil Kozhemiachenko, Igor Sedlár

Potential Business Impact:

Helps computers reason about uncertain ideas.

Business Areas:
Natural Language Processing Artificial Intelligence, Data and Analytics, Software

Modal probabilistic logics provide a framework for reasoning about probability in modal contexts, involving notions such as knowledge, belief, time, and action. In this paper, we study a particular family of these logics, extending the modal Łukasiewicz many-valued logic. These logics are shown to be capable of expressing nuanced probabilistic concepts, including upper and lower probabilities. Our main contribution is a PSPACE-completeness result for two variants of the local consequence problem, providing a precise computational characterisation.

Page Count
15 pages

Category
Computer Science:
Logic in Computer Science