In recent years, neural systems have demonstrated highly effective learning ability and
superior perception intelligence. However, they have been found to lack effective reasoning …

Large Language Models (LLMs) have shown human-like reasoning abilities but still struggle
with complex logical problems. This paper introduces a novel framework, Logic-LM, which …

Reasoning, a crucial ability for complex problem-solving, plays a pivotal role in various real-
world settings such as negotiation, medical diagnosis, and criminal investigation. It serves …

Large language models (LLMs) such as ChatGPT have recently demonstrated significant
potential in mathematical abilities, providing valuable reasoning paradigm consistent with …

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 …

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 …

Advancements in Artificial Intelligence (AI) and deep neural networks have driven significant
progress in vision and text processing. However, achieving human-like reasoning and …

Recently there are great efforts on leveraging machine learning and logical reasoning. Many
approaches start from a given knowledge base, and then try to utilize the knowledge to help …