NeuS 2024 aimed to bring together novel concepts, theories, and practices that helped in the development of the science and application of neuro-symbolic computing and systems. The conference focused on system theoretic approaches to address fundamental challenges to increase the confidence of neuro-symbolic systems by making them more secure, dependable, and trustworthy. An emphasis was placed on design, analysis, and synthesis techniques for neuro-symbolic systems with a particular interest in assurance metrics, robustness, and ease of translation from design to production.

In doing so, the conference aimed to advance the development of principled approaches to the design, development, and deployment of complex and sophisticated neuro-symbolic systems. Topics of particular interest included:

• Artificial Intelligence

• Autonomy

• Cyber-Physical Systems

• Formal Methods

• Large Language Models

• Machine Learning