In this paper we focus on the problem of making a model of the sensory apparatus from raw uninterpreted sensory data as defined by Pierce and Kuipers (Artificial Intelligence 92:169-227, 1997). The method relies on generic properties of the agent’s world such as piecewise smooth effects of movement on sensory features. We extend a previously described algorithm with an information-theoretic distance metric that can find informational structure not found by the original algorithm. We also use the method to create metric projections of the sensory and motor systems of a robot. Data from a real robot show that the metric projections for example can be used to distinguish the vision sensors from all other sensors and also to find their functional layout. Finally we present an application of the method where the real layout of the vision sensors is found from scrambled vision data. 1.

Identifying and utilizing information is central to reproductive success. We study a scenario where a multicellular colony has to trade-off between utility of strategies for investment in persistence or progeny and the (Shannon-type) relevant information necessary to realize these strategies. We develop a general approach to treat such problems that involve iterated games where utility is determined by iterated play of a strategy and where, in turn, informational processing constraints limit the possible strategies.

We survey and outline how an agent-centered, information-theoretic approach to meaningful information extending classical Shannon information theory by means of utility measures relevant for the goals of particular agents can be applied to sensor evolution for real and constructed organisms. Furthermore, we discuss the relationship of this approach to the programme of freeing artificial life and robotic systems from reactivity, by describing useful types of information with broader temporal horizon, for signaling, communication, affective grounding, two-process learning, individual learning, imitation and social learning, and episodic experiential information (memories, narrative, and culturally transmitted information).

Abstract — A method is presented for adapting the sensors of a robot to the statistical structure of its current environment. This enables the robot to compress incoming sensory information and to find informational relationships between sensors. The method is applied to creating sensoritopic maps of the informational relationships of the sensors of a developing robot, where the informational distance between sensors is computed using information theory and adaptive binning. The adaptive binning method constantly estimates the probability distribution of the latest inputs to maximize the entropy in each individual sensor, while conserving the correlations between different sensors. Results from simulations and robotic experiments with visual sensors show how adaptive binning of the sensory data helps the system to discover structure not found by ordinary binning. This enables the developing perceptual system of the robot to be more adapted to the particular embodiment of the robot and the environment. Index Terms — Ontogenetic robotics, sensory systems, entropy maximization

For several decades experiments have been performed where animals have been reared in environments with orientationally restricted contours. The aim has been to nd out what e ects the visual eld has on the development of the visual system in the brain. In this paper we describe similar experiments performed with a robot acting in an environment with only vertical contours and compare the results with the same robot in an ordinary ofce environment. Using metric projections of the informational distances between sensors it is shown that all visual sensors in the same vertical column are clustered together in the environment with only vertical contours. We also show how the informational structure of the sensors unfold when the robot moves from the environment with oriented contours to a normal environment. 1.

Abstract. We introduce a concise approach to teamwork evaluation on multiple levels — dealing with agent’s behaviour spread and multi-agent coordination potential, and abstracting away the team decision process. The presented quantitative information-theoretic methods measure behavioural and epistemic entropy, and detect phase transitions — the edge of chaos — in team performance. The techniques clearly identify under-performing states, where a change in tactics may be warranted. This approach is a step towards a unified quantitative framework on behavioural and belief dynamics in complex multi-agent systems. 1

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 — We present a new approach to use adaptive AI and Information Theory to aid the evaluation of game mechanics. Being able to evaluate the core game mechanics early during production is useful to improve the quality of a game, and ultimately, player satisfaction. A current problem with automated game evaluation via AI is to define measurable parameters that correlate to the quality of the game mechanics. We apply the Information Theory based concept of “Relevant Information ” to this problem and argue that there is a relation between enjoyment related game-play properties and Relevant Information for an AI playing the game. We also demonstrate, with a simple game implementation, a.) how an adaptive AI can be used to approximate the Relevant Information, b.) how those measurable numerical values relate to certain game design flaws c.) how this knowledge can be used to improve the game.

On behalf of:

Abstract—Empowerment quantifies the choice available to an agent as the actuation channel capacity. However, not all such choices are sustainable: After some choices, the agent may not be able to return to its original state, or returning there may be costly. In this paper we explore whether empowerment can be adapted to obtain a measure of sustainability. As a straightforward modification, the agent’s options is restricted to actions that are reversible within a given time horizon. We furthermore investigate the lengths of return paths and discuss their potential to indicate sustainability. I.