Chaotic itinerancy in coupled dynamical recognizers

A design for living technology: experiments with the mind time machine

Living technology aims to help people expand their experiences in everyday life. The environment offers people ways to interact with it, which we call affordances. Living technology is a design for new affordances. When we experience something new, we remember it by the way we perceive and interact with it. Recent studies in neuroscience have led to the idea of a default mode network, which is a baseline activity of a brain system. The autonomy of artificial life must be understood as a sort of default mode that self-organizes its baseline activity, preparing for its external inputs and its interaction with humans. I thus propose a method for creating a suitable default mode as a design principle for living technology. I built a machine called the mind time machine (MTM), which runs continuously for 10 h per day and receives visual data from its environment using 15 video cameras. The MTM receives and edits the video inputs while it self-organizes the momentary now. Its base program is a neural network that includes chaotic dynamics inside the system and a meta-network that consists of video feedback systems. Using this system as the hardware and a default mode network as a conceptual framework, I describe the system's autonomous behavior. Using the MTM as a testing ground, I propose a design principle for living technology.

Adaptability and diversity in simulated turn-taking behavior

Turn-taking behavior is simulated in a coupled-agents system. Each agent is modeled as a mobile robot with two wheels. A recurrent neural network is used to produce the motor outputs and to hold the internal dynamics. Agents are developed to take turns on a two-dimensional arena by causing the network structures to evolve. Turn taking is established using either regular or chaotic behavior of the agents. It is found that chaotic turn takers are more sensitive in response to inputs from the other agent. Conversely, regular turn takers are comparatively robust against noisy inputs, owing to their restricted dynamics. From many observations, including turn taking with virtual agents, we claim that there is a complementary relationship between robustness and adaptability. Furthermore, by investigating the recoupling of agents from different GA generations, we report the emergence of a new turn-taking behavior. Potential for synthesizing a new form of interaction is another characteristic of chaotic turn takers.