Skip to main content

Deep Learning with Logical Constraints

Eleonora Giunchiglia‚ Mihaela Catalina Stoian and Thomas Lukasiewicz

Abstract

In recent years, there has been an increasing interest in exploiting logically specified background knowledge in order to obtain neural models (i) with a better performance, (ii) able to learn from less data, and/or (iii) guaranteed to be compliant with the background knowledge itself, e.g., for safety-critical applications. In this survey, we retrace such works and categorize them based on (i) the logical language that they use to express the background knowledge and (ii) the goals that they achieve.

Book Title
Proceedings of the 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence‚ IJCAI−ECAI 2022‚ Survey Track‚ Vienna‚ Austria‚ July 23−29‚ 2022
Month
July
Publisher
IJCAI/AAAI Press
Year
2022