(New) CFP Special Track of Journal of Artificial Intelligence Research (JAIR) on "Integration of Logical Constraints in Deep Learning"

Over the last few years, the integration of logical constraints in Deep Learning models has gained significant attention from research communities for its potential to enhance the interpretability, robustness, safety, and generalization capabilities of these models. Looking ahead, challenges in this field extend to the development of Machine Learning models that not only incorporate logical constraints but also provide robust assurances. This involves ensuring that AI systems adhere to specific ( temporal) logical or ethical constraints, offering a level of guarantees in their behavior.

This special track aims to explore and showcase recent advancements in the integration of logical constraints within deep learning models, spanning the spectrum of verification, synthesis, monitoring and explainability, by considering exact and approximate solutions, online and offline approaches. The focus will also extend to encompass innovative approaches that address the challenges associated with handling logical constraints in neural networks. Thus, this special track seeks submissions on the integration of logical constraints into deep learning approaches. Pertinent review papers of exceptional quality may also be considered.