Daniel Polani

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We define a measure of redundant information based on projections in the space of probability distributions. Redundant information between random variables is information that is shared between those variables. But, in contrast to mutual information, redundant information denotes information that is shared about the outcome of a third variable. Formalizing(More)
Sensor evolution in nature aims at improving the acquisition of information from the environment and is intimately related with selection pressure toward adaptivity and robustness. Our work in the area indicates that information theory can be applied to the perception-action loop. This letter studies the perception-action loop of agents, which is modeled as(More)
The classical approach to using utility functions suffers from the drawback of having to design and tweak the functions on a case by case basis. Inspired by examples from the animal kingdom, social sciences and games we propose empowerment, a rather universal function, defined as the information-theoretic capacity of an agent’s actuation channel. The(More)
In biology, the exception is mostly the rule, and the rule is mostly the exception. However, recent results indicate that known universal concepts in biology such as the genetic code or the utilization of ATP as a source of energy may be complemented by a large class of principles based on Shannon's concept of information. The present position paper(More)
This article describes a developmental system based on information theory implemented on a real robot that learns a model of its own sensory and actuator apparatus. There is no innate knowledge regarding the modalities or representation of the sensory input and the actuators, and the system relies on generic properties of the robot’s world such as piecewise(More)
We apply kernel-based methods to solve the difficult reinforcement learning problem of 3vs2 keepaway in RoboCup simulated soccer. Key challenges in keepaway are the highdimensionality of the state space (rendering conventional discretization-based function approximation like tilecoding infeasible), the stochasticity due to noise and multiple learning agents(More)
The perception-action cycle is often defined as “the circular flow of information between an organism and its environment in the course of a sensory guided sequence of actions towards a goal” (Fuster 2001, 2006). The question we address in this paper is in what sense this “flow of information” can be described by Shannon’s measures of information introduced(More)