Full text not available from this repository.

Abstract

This paper introduces Compositional Neural Logic Programming (CNLP), a framework that integrates neural networks and logic programming for symbolic and sub-symbolic reasoning. We adopt the idea of compositional neural networks to represent first-order logic predicates and rules. A voting backward-forward chaining algorithm is proposed for inference with both symbolic and sub-symbolic variables in an argument-retrieval style. The framework is highly flexible in that it can be constructed incrementally with new knowledge, and it also supports batch reasoning in certain cases. In the experiments, we demonstrate the advantages of CNLP in discriminative tasks and generative tasks.

Item Type:	Conference Publication
Authors/Creators:	Tran, SN
Keywords:	deep learning, reasoning
Journal or Publication Title:	Proceedings of the 30th International Joint Conference on Artificial Intelligence
Publisher:	International Joint Conferences on Artificial Intelligence Organization
DOI / ID Number:	10.24963/ijcai.2021/421
Copyright Information:	Copyright 2021 International Joint Conferences on Artificial Intelligence
Item Statistics:	View statistics for this item

Actions (login required)

Item Control Page