Tracking Information Flow through the Environment: Simple Cases of Stigmerg

Recent work in sensor evolution aims at studying the perception-action loop in a formalized information-theoretic manner. By treating sensors as extracting information and actuators as having the capability to "imprint" information on the environment we can view agents as creating, maintaining and making use of various information flows. In our paper we study the perception-action loop of agents using Shannon information flows. We use information theory to track and reveal the important relationships between agents and their environment. For example, we provide an information-theoretic characterization of stigmergy and evolve finite-state automata as agent controllers to engage in stigmergic communication. Our analysis of the evolved automata and the information flow provides insight into how evolution organizes sensoric information acquisition, implicit internal and external memory, processing and action selection

All Else Being Equal Be Empowered

The original publication is available at www.springerlink.com . Copyright Springer DOI : 10.1007/11553090_75The 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 concept applies to any sensorimotoric apparatus. Empowerment as a measure reflects the properties of the apparatus as long as they are observable due to the coupling of sensors and actuators via the environment.Peer reviewe

Empowerment for Continuous Agent-Environment Systems

This paper develops generalizations of empowerment to continuous states. Empowerment is a recently introduced information-theoretic quantity motivated by hypotheses about the efficiency of the sensorimotor loop in biological organisms, but also from considerations stemming from curiosity-driven learning. Empowemerment measures, for agent-environment systems with stochastic transitions, how much influence an agent has on its environment, but only that influence that can be sensed by the agent sensors. It is an information-theoretic generalization of joint controllability (influence on environment) and observability (measurement by sensors) of the environment by the agent, both controllability and observability being usually defined in control theory as the dimensionality of the control/observation spaces. Earlier work has shown that empowerment has various interesting and relevant properties, e.g., it allows us to identify salient states using only the dynamics, and it can act as intrinsic reward without requiring an external reward. However, in this previous work empowerment was limited to the case of small-scale and discrete domains and furthermore state transition probabilities were assumed to be known. The goal of this paper is to extend empowerment to the significantly more important and relevant case of continuous vector-valued state spaces and initially unknown state transition probabilities. The continuous state space is addressed by Monte-Carlo approximation; the unknown transitions are addressed by model learning and prediction for which we apply Gaussian processes regression with iterated forecasting. In a number of well-known continuous control tasks we examine the dynamics induced by empowerment and include an application to exploration and online model learning

A framework for the local information dynamics of distributed computation in complex systems

The nature of distributed computation has often been described in terms of the component operations of universal computation: information storage, transfer and modification. We review the first complete framework that quantifies each of these individual information dynamics on a local scale within a system, and describes the manner in which they interact to create non-trivial computation where "the whole is greater than the sum of the parts". We describe the application of the framework to cellular automata, a simple yet powerful model of distributed computation. This is an important application, because the framework is the first to provide quantitative evidence for several important conjectures about distributed computation in cellular automata: that blinkers embody information storage, particles are information transfer agents, and particle collisions are information modification events. The framework is also shown to contrast the computations conducted by several well-known cellular automata, highlighting the importance of information coherence in complex computation. The results reviewed here provide important quantitative insights into the fundamental nature of distributed computation and the dynamics of complex systems, as well as impetus for the framework to be applied to the analysis and design of other systems.Comment: 44 pages, 8 figure

Impoverished empowerment : `meaningful' action sequence generation through bandwidth limitation

“The original publication is available at www.springerlink.com” Copyright SpringerEmpowerment 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 and assumes it can be fully explored. This may be too aggressive an assumption and here we explore impoverished versions achieved through a limit on the bandwidth of the empowerment generating action sequences. It turns out that limited richness of actions concentrate on the most important" ones with the additional benefit that the empowerment horizon can be extended drastically into the fu- ture. This may indicate a path towards and intrinsical preselection for preferred behaviour sequences and may help to suggest more biologically plausible approaches.Peer reviewe

Empowerment: a universal agent-centric measure of control

Peer reviewe

Keep Your Options Open : An Information-Based Driving Principle for Sensorimotor Systems

Copyright: 2008 Klyubin et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. http://www.plosone.org/ DOI: 10.1371/journal.pone.0004018The 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 organism model. It measures the potential of the organism to imprint information on the environment via its actuators in a way that can be recaptured by its sensors, essentially quantifying the options available and visible to the organism. Various scenarios suggest that such a quantity could identify the preferred direction of evolution or adaptation of the sensorimotor loop of organisms.Peer reviewe

Organization of the information flow in the perception-action loop of evolved agents

Sensor evolution in nature aims at improving the acquisition of information from the environment and is intimately related with selection pressure towards adaptivity and robustness. Recent work in the area aims at studying the perception-action loop in a formalized information-theoretic manner This paves the way towards a principled and general understanding of the mechanisms guiding the evolution of sensors in nature and provides insights into the design of mechanisms of artificial sensor evolution. In our paper we study the perception-action loop of agents. We evolve finite-state automata as agent controllers to solve an information acquisition task in a simple virtual world and study how the information flow is organized by evolution. Our analysis of the evolved automata and the information flow provides insight into how evolution organizes sensoric information acquisition, memory, processing and action selection. In addition, the results are compared to ideal information extraction schemes following from the Information Bottleneck principle

Making sense of the sensory data - coordinate systems by hierarchical decomposition

"The original publication is available at www.springerlink.com" Copyright SpringerHaving the right sensory channels is an important ingredient for building an autonomous agent, but we still have the problem of making sense of the sensory data for the agent. This is the basic problem of artificial intelligence. Here we propose an algebraic method for generating abstract coordinate system representations of the environment based on the agent’s actions. These internal representations can be refined and regenerated during the lifespan of the agent.Peer reviewedFinal Accepted Versio

Information-Theoretic Modeling of Sensory Ecology: Channels of Organism-Specific Meaningful Information

Full text of this item is not available in the UHRA.Information theory developed by C. Shannon and his followers in the mathematical theory of communication surprisingly but successfully abstracted away from two questions: (1) the origin and maintenance of information channels, and (2) the meaning of information. However, in understanding the evolutionary sensory ecology of the many information channels used by particular organisms, we are confronted with exactly these issues. What are the evolutionary and developmental origins of particular channels in an embodied organism? How do they benefit the organism? Why this type of sensor and not another? Sensors are costly to build, maintain, operate and carry. The costs and benefits of access to particular information have an impact on survival and reproductive success within a particular ecological context. We introduce a framework in which channels of information meaningful to an organism (sensors, actuators, and internal channels within it, and between it and other organisms, and/or between an organism and its environment) can each be treated using an extended Shannon information theory. Rigorous information metrics relate informational channels to organism-specific relevance and utility