Neuro-Symbolic Agent Swarm with Memory Consolidation and Neural Coupling: A Technical Dive into Advanced AI Architectures

Abstract

Recent advancements in artificial intelligence (AI) show that combining neural and symbolic reasoning has opened doors to a more robust and versatile frameworks for intelligent agents. Neuro-symbolic systems are at the forefront of solving complex tasks that require both high-level reasoning and low-level learning.

This technical article introduces a Neuro-Symbolic Agent Swarm enhanced with Memory Consolidation and Neural Coupling, focusing on multi-agent collaboration in dynamic environments. The article will explore the mathematical underpinnings, thoroughly dissect the code implementation, and discuss the theoretical and practical use cases and advantages over traditional AI systems.

This comprehensive guide caters to researchers and developers working at the intersection of neural networks, reinforcement learning and symbolic reasoning. It discusses the technical components behind the agent swarm, elucidating the strengths of integrating memory consolidation with a multi-agent neural coupling mechanism. We will explore mathematical theorems guiding the architecture, while also breaking down the code and analysing the core algorithms. Finally, we will evaluate its…