State Abstraction Techniques for Complex Cognitive AI Agents

Abstract

State abstraction techniques play a critical role in enabling cognitive AI agents to operate effectively
in complex, dynamic environments. By transforming high-dimensional perceptual inputs into
structured relational, hierarchical, and temporal representations, abstraction reduces computational
overhead while preserving the essential semantics required for robust long-horizon decision-making.
This article presents a multi-level abstraction framework that enhances policy stability, generalization
efficiency, and resilience to environmental perturbations. The analysis highlights how abstraction
supports scalable reasoning, maintains behavioral interpretability, and balances strategic planning with
responsive action execution. These results indicate that state abstraction is a necessary foundation for
building cognitively coherent and operationally reliable autonomous decision-making systems.