Computational intelligence: prediction, control and memory in artificial and biological agents

All non-linear dynamical systems produce computations by storing and transforming input information. A computing device is a physical system that can be reliably controlled into transducing specific computations. Intelligent agents, computing devices themselves, manipulate and exploit their physical environment to achieve their goals. Goals are future states of the world with a specific information content that is desired by the agent. In this setting, the agent's behavior can be interpreted as the tentative manipulation of a computing device (the environment) into the configuration that will output the desired information. We call computational intelligence the ability to control the physical environment into specific informational states. We define prediction and control as basic-intelligence and call meta-intelligence the collection of computational tools necessary to exhibit basic intelligence in a non-stationary world using a finite physical computing device. In this dissertation I develop a framework to model the computational intelligence exhibited by biological and artificial agents as they control their context to achieve their goals. We build on the original levels of understanding manifesto from Marr and Poggio and use the same levels: computational, algorithmic, and implementational—to develop a theory of intelligence that is agnostic about the physical substrate used to compute. Differently from the purely descriptive intentions of the original formulation we take a normative approach. Starting from assumptions on the causal architecture of the world and the perceptual tools of the agents, we define the computational requirement to satisfy for basic intelligence. With a few additional assumptions on the relationship between computations and the hardware used to implement them, we derive multiple meta-intelligence requirements for agents that live in non-stationary and diverse environments.