We are interested in designing artificial universes for artifi- cial agents. We view artificial agents as networks of high- level processes on top of of a low-level detailed-description system. We require that the high-level processes have some intri
nsic explanatory power and we introduce an extension of informational closure namely interaction closure to capture this. Then we derive a method to design artificial universes in the form of finite Markov chains which exhibit high-level pro- cesses that satisfy the property of interaction closure. We also investigate control or information transfer which we see as an building block for networks representing artificial agents.
This book chapter is an introduction to and an overview of the information-theoretic, task independent utility function Empowerment, which is defined as the channel capacity between an agents actions and an agents sensors. It quantifies how much infl
uence and control an agent has over the world it can perceive. This book chapter discusses the general idea behind empowerment as an intrinsic motivation and showcases several previous applications of empowerment to demonstrate how empowerment can be applied to different sensor-motor configuration, and how the same formalism can lead to different observed behaviors. Furthermore, we also present a fast approximation for empowerment in the continuous domain.
