In this paper, we discuss previous research in automatic prediction of court decisions. We
define the difference between outcome identification, outcome-based judgement …

To utilize the raw inputs and symbolic knowledge simultaneously, some recent neuro-
symbolic learning methods use abduction, ie, abductive reasoning, to integrate sub …

Most systems integrating data-driven machine learning with knowledge-driven reasoning
usually rely on a specifically designed knowledge base to enable efficient symbolic …

Deep learning for Earth imagery plays an increasingly important role in geoscience
applications such as agriculture, ecology, and natural disaster management. Still, progress …

Integrating complementary strengths of raw data and logical rules to improve the learning
generalization has been recently shown promising and effective, eg, abductive learning is …

Neuro-symbolic hybrid systems are promising for integrating machine learning and symbolic
reasoning, where perception models are facilitated with information inferred from a symbolic …

Abductive Learning is a framework that combines machine learning with first-order logical
reasoning. It allows machine learning models to exploit complex symbolic domain …

Abstract Optical Character Recognition (OCR) of historical document images remains a
challenging task because of the distorted input images, extensive number of uncommon …

The general mitigating and aggravating circumstances of criminal liability are elements
attached to the crime that, when they occur, affect the punishment quantum. Cuban criminal …

Evidences are important proofs to support judicial trials. Automatically extracting evidences
from judgement documents can be used to assess the trial quality and support “Intelligent …