A Mathematical and Cognitive Framework for Emotionally-Aware Self-Reflective AGI Systems

This paper introduces a novel theoretical framework for Artificial General Intelligence (AGI) systems capable of emotional understanding, self-reflection, and continuous learning. Building upon concepts from information theory, Bayesian inference, and topological cognitive modeling, we formalize three core constructs: self-uncertainty, emotional abstraction, and reflective loop triggers. Self-uncertainty quantifies the system’s internal doubt about its perceptions or actions, expressed via a conditional entropy-KL divergence formula. Emotional abstraction maps raw sensory inputs to a continuous affective manifold, enabling the system to generalize emotional states across contexts using concept-weighted graphs. Reflective loops are triggered dynamically based on an adaptive threshold tied to prediction errors and epistemic surprise. Together, these constructs allow an AGI to pause automatic behaviors, introspect, and update its internal models based on mismatched predictions or novel emotions. We propose a preliminary architecture combining lightweight transformer encoders, attention-based meta-cognitive modules, and low-dimensional emotion embeddings. We present preliminary simulation results in a simplified GridWorld environment, showcasing the system's ability to trigger self-reflection based on uncertainty. This framework moves toward closing the gap between machine intelligence and human-like awareness by embedding mechanisms for uncertainty management and emotional reasoning into core cognition.

کلیدواژه ها:

AGI ، Self-Uncertainty ، Emotional Embedding ، Reflective Cognition ، Continual Learning