Meta-cognitive agents via fusion of cognitive architectures and LLMs
Fuse cognitive architectures such as ACT-R, Soar, or Sigma with large language models to develop meta-cognitive agents capable of self-monitoring, evaluation, and adaptive adjustment of reasoning and learning.
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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.