Measuring Entropy in Embodied Neural Agents with Homeostasic Units: A Link Between Complexity and Cybernetics

Abstract. We present a model of a recurrent neural network with homeostasic units, embodied in a minimalist articulated agent with a single link and joint. The configuration of the agent is determined by the total activation level or kinetic energy of the network. We study the complexity patterns of the neural networks, and see how the entropy of the neural controller state and agent configuration changes with the relative characteristic time of the homeostasis when compared with the excitatoryinhibitory activation dynamics of network. We also present a meta-model of embodied neural agents, that serves as conceptual framework to study self-perturbation and the self-organization in embodied neural agents. Simulation results show that homeostasis significantly influences the dynamics of the network and the controlled agent, allowing the system to escape fixed-points and produce complex aperiodic behavior. The relation between the characteristic time of homeostasis and the characteri...

• Type: Conference
• Year: 2007
• Where: ECAL
• Authors: Jorge Simão
• Keywords: Artificial Intelligence, Characteristic Time, ECAL 2007, Embodied Neural Agents, Neural