Fingerprinting Agent-Environment Interaction via Information Theory

In this paper, we investigate by means of statistical and information-theoretic measures, to what extent sensory-motor coordinated activity can generate and structure information in the sensory channels of a simulated agent interacting with its surrounding environment. We show how the usage of correlation, entropy, and mutual information can be employed (a) to segment an observed behavior into distinct behavioral states, (b) to quantify (fingerprint) the agent-environment interaction, and (c) to analyze the informational relationship between the different components of the sensory-motor apparatus. We hypothesize that a deeper understanding of the information-theoretic implications of sensory-motor coordination can help us endow our robots with better sensory morphologies, and with better strategies for exploring their surrounding environment

Information Theoretic Implications of Embodiment for Neural Network Learning

. The traditional view of neural networks is algorithmic. The general learning problems are typically hard and powerful networks and learning algorithms such as MLPs with BP must be used. It is a well-known fact that the power of the learning algorithm required to solve a problem depends on the statistical distribution of the input data. If the distribution is known, a "taylored" network can be used. We argue that in the real-world the distributions are not given, but can be generated in the process of sensory-motor coordination as the embodied autonomous agent interacts with its environment. It is shown that sensory-motor coordination can lead to dramatic reduction of learning comlexity in the information theoretic sense. The ideas discussed in this paper tie in with a set of design principles for autonomous agents that we have established over the last few years. 1 Introduction More than 40 years ago Ashby (1956) has pointed out that constraints are the basis of learning: "learning ..