Abstract — Information theoretic methods are used to characterise and identify robot-environment interactions, with a view to using these to build an embodied interaction history from the robot’s perspective. A bottom-up approach is taken using uninterpreted raw sensor and motor data. Interactions are analysed by calculating the Average Information Distance (AID) between all sensors and motors over a moving time window and used to create 2-dimensional “phase-plots ” that can be thought of as describing the current interaction. Sensor-Motor AID Phase-plots are shown to be able to distinguish simple behaviours among a sequence of behaviours.

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In nature, sensors evolve to capture relevant information needed for organisms of a particular species to survive and reproduce. In this paper we study how sensor layouts may evolve in different environments and under pressure of different informational constraints. To do this we evolve sensor layouts for different environments and constraints using a fitness measure with weighted terms for redundancy and novelty, using, respectively, mutual information and Crutchfield 's information metric. The results show how different sensor layouts evolve depending on the structure and complexity of the environment but also how selective pressure for redundancy or novelty might affect the design.

Abstract. Anticipation is one of the key aspects involved in flexible and adaptive behavior. The ability for an autonomous agent to extract a relevant model of its coupling with the environment and of the environment itself can provide it with a strong advantage for survival. In this work we develop an event-based anticipation framework for performing latent learning and we provide two mathematical tools to identify relevant relationships between events. These tools allow us to build a predictive model which is then embedded in an action-selection architecture to generate adaptive behavior. We first analyze some of the properties of the model in simple learning tasks. Its efficiency is evaluated in a more complex task where the agent has to adapt to a changing environment. In the last section we discuss extensions of the model presented. 1

How can a planet (or a dust cloud) produce Microbes, Minds, Mathematics, Music, Marmite (along with murder, religious bigotry, and other nastiness). An introduction to the Meta-Morphogenesis project (Partly inspired by Turing’s work on morphogenesis)