Bridging Paradigms: The Integration of Symbolic and Connectionist AI in LLM-Driven Autonomous Agents

Abstract

This paper explores the integration of symbolic and connectionist paradigms within the realm of Large Language Model (LLM)-powered autonomous agents, highlighting the complementary strengths of each approach. Symbolic AI, known for its structured, rule-based logic, excels at encoding explicit knowledge and facilitating reasoning, while connectionist AI, particularly neural networks, provides robustness in handling large-scale unstructured data through learning from examples. By merging these paradigms, we propose a synergistic framework that enhances autonomous agent capabilities in both reasoning and adaptability. We investigate how LLMs, which exhibit traits of both paradigms, can serve as the backbone for this integration, fostering improved decision-making, natural language understanding, and autonomy. Our findings underscore the potential of this hybrid approach to advance the development of intelligent agents that can navigate complex environments, reason effectively, and learn from experience in dynamic, real-world applications.