Outsourcing Control requires Control Complexity

TL;DR

It is observed that the agents first have to understand the relevant dynamics of the environment to interact well with their surroundings, and an increased controller complexity can facilitate a better interaction between an agent’s body and its environment.

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.

Figures (11)

• Figure 1: Sketch of an agent interacting with its environment and the different measured information flows.
• Figure 2: Sketch of a two-wheeled agent and its four movements in its environment on the left. Five different agents in their environment in the middle and the possible sensor length from 0.5 on the top right to 2 on the bottom right.
• Figure 3: Sketch of the architecture of an internal-world-model agent on the left and the sensorimotor loop of this agent on the right.
• Figure 4: The connections of the complete and split ideal-world-model agents on the top and the complete and split internal-world-model agents on the bottom.
• Figure 5: Graphs corresponding to the split systems in case of $\Phi_{IIT}$ in (A), $\Psi_{SI}$ and $\Psi_C$ in (B), $\Psi_{SynP}$ in (C) and $\Psi_{MC}$ in (D).