Plan-SOFAI: A Neuro-Symbolic Planning Architecture

Abstract

The notion of Artificial Intelligence (AI) has garnered significant attention in recent years and AI-based tools have increasingly become integrated into our daily lives. As this strand of research is gaining traction, one of the central debates is whether end-to-end Machine Learning or symbolic AI approaches alone can lead to an effective AI model, or if these techniques need to be integrated into a synergistic system. We believe the integration route to be the most promising. To this end, we introduce a specialization of a neuro-symbolic architecture, known as SOFAI (Slow and Fast AI), inspired by the cognitive framework popularized by D. Kahneman's book "Thinking, Fast and Slow". Our system, referred to as Plan-SOFAI, aims to tackle planning problems across a large spectrum of scenarios, with a specific focus on the classical setting. Plan-SOFAI leverages multiple planning approaches, each possessing distinct characteristics and categorized as either fast or slow while incorporating a metacognitive process for governance. Finally, we evaluated the performance of this system against state-of-the-art planners, demonstrating that our exhibits a solid balance between solving speed and plans' optimality.