Neuro-Symbolic AI for Military Applications

TL;DR

The paper investigates Neuro-Symbolic AI as a hybrid of neural learning and symbolic reasoning to enhance military decision-making, intelligence analysis, and autonomous systems while addressing ethical, legal, and technical risks. It introduces a learning-cycle framework and emphasizes knowledge graphs and hybrid architectures to support explainable, robust reasoning in dynamic environments. The discussion covers autonomy in LAWS and NLAWS, practical military applications, and illustrative case studies such as ANSR, the DG concept, and RAID, alongside the need for rigorous V&V and transparent decision-making. Overall, the work highlights both the strategic value and governance challenges of Neuro-Symbolic AI in defense, and points to future directions focused on adaptable, human-centered collaboration and resilient autonomous systems.

Abstract

Artificial Intelligence (AI) plays a significant role in enhancing the capabilities of defense systems, revolutionizing strategic decision-making, and shaping the future landscape of military operations. Neuro-Symbolic AI is an emerging approach that leverages and augments the strengths of neural networks and symbolic reasoning. These systems have the potential to be more impactful and flexible than traditional AI systems, making them well-suited for military applications. This paper comprehensively explores the diverse dimensions and capabilities of Neuro-Symbolic AI, aiming to shed light on its potential applications in military contexts. We investigate its capacity to improve decision-making, automate complex intelligence analysis, and strengthen autonomous systems. We further explore its potential to solve complex tasks in various domains, in addition to its applications in military contexts. Through this exploration, we address ethical, strategic, and technical considerations crucial to the development and deployment of Neuro-Symbolic AI in military and civilian applications. Contributing to the growing body of research, this study represents a comprehensive exploration of the extensive possibilities offered by Neuro-Symbolic AI.

Figures (6)

• Figure 1: The Knowledge Base contains rules and facts which are used to represent explicit knowledge. Rules and Facts represent logical statements or heuristics that encode knowledge and guide reasoning. The Expert System utilizes the knowledge base and reasoning engine to make decisions or provide solutions. The Reasoning Engine involves the process of using logical inference to draw conclusions or perform tasks based on the rules and facts in the knowledge base. The final Output represents the results or decisions made by the Symbolic AI system based on its knowledge and rules.
• Figure 2: Data is fed into the network as input, and it processes the input data through multiple layers of neurons with adjustable weights. The network learns from data to produce output, making it well-suited for tasks involving pattern recognition and complex data processing.
• Figure 3: Taxonomy of the main topics covered in this paper.
• Figure 4: An Example of a Neuro-Symbolic AI Architecture. Note that this is one of many possible architectures in the field.
• Figure 5: The learning cycle of Neuro-Symbolic AI systems bader2005dimensions.