Artificial Intelligence (AI) has emerged as a complementary technology in supply chain
research. However, the majority of AI approaches explored in this context afford little to no …

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

Chain-of-thought prompting has demonstrated remarkable performance on various natural
language reasoning tasks. However, it tends to perform poorly on tasks which requires …

We explore how generating a chain of thought---a series of intermediate reasoning steps---
significantly improves the ability of large language models to perform complex reasoning. In …

Large language models (LLMs) have shown remarkable reasoning capabilities given chain-
of-thought prompts (examples with intermediate reasoning steps). Existing benchmarks …

We introduce DeepProbLog, a probabilistic logic programming language that incorporates
deep learning by means of neural predicates. We show how existing inference and learning …

Although deep reinforcement learning has become a promising machine learning approach
for sequential decision-making problems, it is still not mature enough for high-stake domains …

We propose a novel framework seamlessly providing key properties of both neural nets
(learning) and symbolic logic (knowledge and reasoning). Every neuron has a meaning as a …

Neuro-symbolic and statistical relational artificial intelligence both integrate frameworks for
learning with logical reasoning. This survey identifies several parallels across seven …

Abstract The latest Deep Learning (DL) models for detection and classification have
achieved an unprecedented performance over classical machine learning algorithms …