Symbolic reasoning with meta-reinforcement learning for complex decision-making

Integrate symbolic reasoning with meta-reinforcement learning to enable complex decision-making in Neuro-Symbolic AI, combining structured knowledge with adaptive learning strategies.

Background

The paper reviews initial efforts that combine reinforcement learning with symbolic features and logic induction. The authors explicitly state that integrating symbolic reasoning with meta-reinforcement learning for complex decision-making remains an open research question.

References

Open research questions remain around how Neuro-Symbolic AI can integrate symbolic reasoning with meta-reinforcement learning for complex decision-making, fuse cognitive architectures with LLMs to develop meta-cognitive agents, leverage LLMs to enhance instance-based learning through meta-cognitive signals, create adaptive meta-cognitive frameworks for real-time conflict resolution, combine modular and agency approaches to build meta-cognitive AI systems aligned with the Common Model of Cognition, improve few-shot learning with cognitive architectures for meta-cognitive awareness, and develop Neuro-Symbolic generative networks that replicate human-like meta-cognitive processes.

— Neuro-Symbolic AI in 2024: A Systematic Review  (2501.05435 - Colelough et al., 9 Jan 2025) in Section 4.6 Meta-Cognition