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Neuro-symbolic artificial intelligence: a survey
The goal of the growing discipline of neuro-symbolic artificial intelligence (AI) is to develop AI systems with more human-like reasoning...
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Collaborative Decision Support Systems Based on Neuro-Symbolic Artificial Intelligence: Problems and Generalized Conceptual Model
AbstractThe development of artificial intelligence technologies and the growing complexity of decision-making when managing complex dynamic systems...
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Neuro-Symbolic Reasoning for Multimodal Referring Expression Comprehension in HMI Systems
Conventional Human–Machine Interaction (HMI) interfaces have predominantly relied on GUI and voice commands. However, natural human communication...
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Reduced implication-bias logic loss for neuro-symbolic learning
Integrating logical reasoning and machine learning by approximating logical inference with differentiable operators is a widely used technique in the...
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Learning explanatory logical rules in non-linear domains: a neuro-symbolic approach
Deep neural networks, despite their capabilities, are constrained by the need for large-scale training data, and often fall short in generalisation...
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Neuro-Symbolic AI + Agent Systems: A First Reflection on Trends, Opportunities and Challenges
To get one step closer to “human-like” intelligence, we need systems capable of seamlessly combining the neural learning power of symbolic feature...
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Neuro Symbolic Reasoning and Learning
This book provides a broad overview of the key results and frameworks for various NSAI tasks as well as discussing important application areas. This...
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New Ideas in Neuro Symbolic Reasoning and Learning
Neuro symbolic reasoning and learning is a topic that combines ideas from deep neural networks with symbolic reasoning and learning to overcome...
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Neuro Symbolic Applications
Although these days neural networks and deep learning get equated with AI, there are many systems that combine neural reasoning and learning with...
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Neuro Symbolic Reasoning with Ontological Networks
In this chapter we describe neuro symbolic approaches developed for ontological domains, focusing mainly on Recurrent Reasoning Networks (RRNs), a...
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NeuroSynt: A Neuro-symbolic Portfolio Solver for Reactive Synthesis
We introduce NeuroSynt, a neuro-symbolic portfolio solver framework for reactive synthesis. At the core of the solver lies a seamless integration of...
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ECIR 23 Tutorial: Neuro-Symbolic Approaches for Information Retrieval
This tutorial will provide an overview of recent advances on neuro-symbolic approaches for information retrieval. A decade ago, knowledge graphs and...
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ADAM: A Prototype of Hierarchical Neuro-Symbolic AGI
Intelligent agents are characterized primarily by their far-sighted expedient behavior. We present a working prototype of an intelligent agent (ADAM)...
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Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework
In this paper we introduce Detect, Understand, Act (DUA), a neuro-symbolic reinforcement learning framework. The Detect component is composed of a...
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Mitigating Data Sparsity via Neuro-Symbolic Knowledge Transfer
Data sparsity is a well-known historical limitation of recommender systems that still impacts the performance of state-of-the-art approaches. The...
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A Vertical-Horizontal Integrated Neuro-Symbolic Framework Towards Artificial General Intelligence
Neuro-symbolic technologies with vertical and horizontal approaches are important for the development of Artificial General Intelligence (AGI). But...
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Deep Explainable Relational Reinforcement Learning: A Neuro-Symbolic Approach
Despite its successes, Deep Reinforcement Learning (DRL) yields non-interpretable policies. Moreover, since DRL does not exploit symbolic relational...
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Towards Explainable Public Sector AI: An Exploration of Neuro-Symbolic AI and Enterprise Modeling (Short Paper)
Artificial Intelligence (AI) offers transformative potential for enhancing public sector services. However, the lack of explainability within many AI...
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TON-ViT: A Neuro-Symbolic AI Based on Task Oriented Network with a Vision Transformer
The objective of this paper is to present a neuro-symbolic AI based technique to represent field-medicine knowledge, referred as to TON-ViT. TON-ViT...
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ReOnto: A Neuro-Symbolic Approach for Biomedical Relation Extraction
Relation Extraction (RE) is the task of extracting semantic relationships between entities in a sentence and aligning them to relations defined in a...