Ultimate Intelligence Part III: Measures of Intelligence, Perception and Intelligent Agents
This work addresses foundational issues in AI and cognitive science by proposing a unified framework for intelligence, perception, and agent modeling, which is foundational rather than incremental.
The paper tackles the problem of modeling perception and intelligent agents by proposing operator induction as a model of perception and reducing universal agent models to it, resulting in a universal measure of fitness applied to reinforcement learning and homeostasis agents based on the free energy principle.
We propose that operator induction serves as an adequate model of perception. We explain how to reduce universal agent models to operator induction. We propose a universal measure of operator induction fitness, and show how it can be used in a reinforcement learning model and a homeostasis (self-preserving) agent based on the free energy principle. We show that the action of the homeostasis agent can be explained by the operator induction model.