Estym

Estym is a conceptual framework that emerged from early discussions within the fields of artificial intelligence and cognitive science. It proposes a way to understand and model the internal states and predictive capabilities of intelligent agents. The core idea behind estym is to represent an agent's understanding of its environment and its own internal processes as a dynamic, evolving estimation. This estimation is not a static snapshot but a continuously updated model that informs decision-making and action.

The term itself is a portmanteau, often understood to combine "estimation" and "system" or "symbiosis," highlighting the interplay between internal modeling and interaction with the external world. Proponents of estym suggest it can be useful for developing more robust and adaptable AI systems, particularly those that need to operate in complex and uncertain environments. By maintaining an ongoing estimation of various factors, an agent can better anticipate future states, evaluate potential outcomes of its actions, and learn from discrepancies between its predictions and reality.

While not a formally defined algorithm, estym serves as a guiding principle for designing agents that can exhibit behaviors such as self-awareness, planning, and generalization. It emphasizes the importance of internal world models and the continuous refinement of these models through experience. The concept encourages thinking about how an agent "feels" about its situation, not in an emotional sense, but in terms of its confidence in its current estimations and its predictions for the future.