Paraconsistent Abductive Learning for Processing Inconsistent Information

The ABductive Learning (ABL) framework aims to bridge the perception and reasoning capabilities of artificial intelligence (AI) by unifying machine learning and logic programming. While the machine learning component classifies symbolic labels from datasets, the logic programming aspect reasons with these labels using a knowledge base, correcting misclassifications. However, the original ABL framework relies on classical logic, which inadequately handles inconsistent information, a common occurrence in knowledge bases. This paper introduces an initial integration of paraconsistent logic programming with abductive learning, called Paraconsistent ABductive Learning (PABL), to enable reasoning among inconsistent information. An experiment on the MNIST single-digit addition task illustrates our approach, showing that our ABL extension maintains a state-of-the-art accuracy of 98.1%. The implementation of our proposed model is publicly available at https://github.com/LiuBodan/PABL.