Gosia Lazuka, Andreea Simona Anghel, et al.
SC 2024
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.
Gosia Lazuka, Andreea Simona Anghel, et al.
SC 2024
Yidi Wu, Thomas Bohnstingl, et al.
ICML 2025
Ben Fei, Jinbai Liu
IEEE Transactions on Neural Networks
Robert Farrell, Rajarshi Das, et al.
AAAI-SS 2010