This theory defines an architecture in which reasoning processes are enhanced by a self-monitoring metacognitive layer. It dynamically adjusts inference strategies based on internal feedback, enabling adaptive logic refinement and situational awareness in intelligent agents.
本理論は、推論プロセスに自己監視型のメタ認知層を導入する構造を定義します。内部フィードバックに基づいて推論戦略を動的に調整し、知的エージェントにおける論理の適応的洗練と状況認識を可能にします。
This model is applicable to cognitive robotics, AI diagnosis systems, and autonomous agents requiring real-time introspection and self-improvement through inference feedback loops.
It can be adapted for personalized learning systems, decision-support tools, and digital assistants that require adjustment of reasoning paths based on user behavior and context.