In this paper, we use empowerment, a recently introduced biologically inspired measure, to allow an AI player to assign utility values to potential future states within a previously unencountered game without requiring ...

Embodied agents can be conceived as entities perceiving and acting upon an external environment. Probabilistic models of this perception-action loop have paved the way to the investigation of information-theoretic aspects ...

Shannon's information theory can be used to quantify morphological and topological features of a collective of agents in an arbitrary environment. In particular the ability of individual agents to extract information locally ...

Hierarchical structuring of behaviour is prevalent in natural and artificial agents and can be shown to be useful for learning and performing tasks. To progress systematic understanding of these benefits we study the effect ...

Empowerment is a promising concept to begin explaining how some biological organisms may assign apriori values expectations to states in taskless scenarios. Standard empowerment samples the full richness of an environment ...

We extend existing models and methods for the informational treatment of the perception-action loop to the case of goaloriented behaviour and introduce the notion of relevant goal information as the amount of information ...

We consider two agents, each equipped with a controller. When they achieve a joint goal configuration, their coordination can be measured informationally. We show that the amount of coordination that two agents need to ...

The central resource processed by the sensorimotor system of an organism is information. We propose an information-based quantity that allows one to characterize the efficiency of the perception-action loop of an abstract ...

We investigate the correlation between the information theoretic measure of empowerment and the graph theoretic measure of closeness centrality, to better understand the structural conditions that must exist in a world for ...