Recent advances in trustworthy explainable artificial intelligence: Status, challenges, and perspectives
Artificial intelligence (AI) and machine learning (ML) have come a long way from the earlier
days of conceptual theories, to being an integral part of today's technological society. Rapid …
days of conceptual theories, to being an integral part of today's technological society. Rapid …
Neuro-symbolic artificial intelligence: The state of the art
Neuro-symbolic AI is an emerging subfield of Artificial Intelligence that brings together two
hitherto distinct approaches.” Neuro” refers to the artificial neural networks prominent in …
hitherto distinct approaches.” Neuro” refers to the artificial neural networks prominent in …
Neuro-symbolic artificial intelligence: Current trends
Neuro-Symbolic Artificial Intelligence–the combination of symbolic methods with methods
that are based on artificial neural networks–has a long-standing history. In this article, we …
that are based on artificial neural networks–has a long-standing history. In this article, we …
Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning
Current advances in Artificial Intelligence and machine learning in general, and deep
learning in particular have reached unprecedented impact not only across research …
learning in particular have reached unprecedented impact not only across research …
Chapter 1. Neural-Symbolic Learning and Reasoning: A Survey and Interpretation 1
The study and understanding of human behaviour is relevant to computer science, artificial
intelligence, neural computation, cognitive science, philosophy, psychology, and several …
intelligence, neural computation, cognitive science, philosophy, psychology, and several …
[HTML][HTML] Neural, symbolic and neural-symbolic reasoning on knowledge graphs
Abstract Knowledge graph reasoning is the fundamental component to support machine
learning applications such as information extraction, information retrieval, and …
learning applications such as information extraction, information retrieval, and …
Facts as experts: Adaptable and interpretable neural memory over symbolic knowledge
Massive language models are the core of modern NLP modeling and have been shown to
encode impressive amounts of commonsense and factual information. However, that …
encode impressive amounts of commonsense and factual information. However, that …
Knowledge extraction and insertion to deep belief network for gearbox fault diagnosis
Deep neural network (DNN) with a complex structure and multiple nonlinear processing
units has achieved great success for feature learning in machinery fault diagnosis. Due to …
units has achieved great success for feature learning in machinery fault diagnosis. Due to …
Deep logic networks: Inserting and extracting knowledge from deep belief networks
Developments in deep learning have seen the use of layerwise unsupervised learning
combined with supervised learning for fine-tuning. With this layerwise approach, a deep …
combined with supervised learning for fine-tuning. With this layerwise approach, a deep …
Learning by applying: A general framework for mathematical reasoning via enhancing explicit knowledge learning
Mathematical reasoning is one of the crucial abilities of general artificial intelligence, which
requires machines to master mathematical logic and knowledge from solving problems …
requires machines to master mathematical logic and knowledge from solving problems …