Outsourcing Control requires Control Complexity

TL;DR

This paper investigates how the complexity of an agent's controller interacts with its morphological computation and environment understanding, revealing a trade-off and the importance of controller complexity for effective interaction.

Contribution

It introduces a framework combining information-theoretic measures to analyze the relationship between controller complexity and morphological computation in embodied agents.

Findings

Higher controller complexity improves environment interaction.

Morphological adaptation reduces the need for complex controllers.

Controller complexity and morphological computation influence each other.

Abstract

An embodied agent constantly influences its environment and is influenced by it. We use the sensorimotor loop to model these interactions and thereby we can quantify different information flows in the system by various information theoretic measures. This includes a measure for the interaction among the agent's body and its environment, called Morphological Computation. Additionally, we examine the controller complexity by two measures, one of which can be seen in the context of the Integrated Information Theory of consciousness. Applying this framework to an experimental setting with simulated agents allows us to analyze the interaction between an agent and its environment, as well as the complexity of its controller, the brain of the agent. Previous research reveals an antagonistic relationship between the controller complexity and Morphological Computation. A morphology adapted well to a task can reduce the necessary complexity of the controller significantly. This creates the problem that embodied intelligence is correlated with a reduced necessity of a controller, a brain. However, in order to interact well with their surroundings, the agents first have to understand the relevant dynamics of the environment. By analyzing learning agents we observe that an increased controller complexity can facilitate a better interaction between an agent's body and its environment. Hence, learning requires an increased controller complexity and the controller complexity and Morphological Computation influence each other.