Synthesizing Robotic AI Spontaneous Behavior via Chaotic Itinerancy

The chaotic itinerancy is a closed-loop pathway through high-dimensional state space of neural activity, directing cortex in sequence with quasi-attractors.

Over the years researchers have created robotic models with attributes, similar to humans. Robotic models which can hear, sense, emotionally support, blink and fight against abuse, are getting heavily researched and deployed in the industry. To expand the further horizon of neuro-robotics, the researchers at the University of Tokyo have created a model that gives robotic AI spontaneous behavior through the chaotic itinerary, a neural process to find in humans and animals. The research paper titled "Designing Spontaneous Behavioural Switching via Chaotic itinerancy" states that the chaotic itinerancy is a high-dimensional non-linear dynamical system, which addresses the pre-existing challenges of cognitive architecture in robotics.

What is Chaotic Itinerancy?

The chaotic itinerancy is a closed-loop pathway through high-dimensional state space of neural activity, directing cortex in sequence with quasi-attractors. The quasi attractor is a region of the brain which has convergent flows as attractants and absorbents actions, and divergent flows, involve repellent and dispersive actions. These flows give ordered periodic activity and disordered chaotic activity between the regions of the brain.

Furthermore, experts have associated quasi-attractors with perception, thoughts, memories, thinking, speaking and writing. Researchers cite that robotics has applied a dynamical systems approach to analyze and control agents associated with training of robots. This approach collaborates the functional hierarchy and the elementary motion by expressing the physical constraints of the agent as the temporal lobe of the brain develops. Following this approach, CI is integrated into model spontaneous behaviors. Moreover, the researchers have proposed an algorithm that designs the properties of CI characterized by neuro-robotics context. This model addresses the challenges of designing a cognitive agent in the conventional context of robotics and artificial intelligence.

Structure of the Model

Researchers prepared a high-dimensional chaotic model, by embedding target quasi-attractors. They used an echo state network which is a form of Recurrent Neural Network and is heavily controlled by reservoir computing. Consequently, internal parameters were added to the model to generate intrinsic complex trajectories. These trajectories are generated by the initial chaotic system also known as innate trajectories which correspond to the types of the discrete input. Parallel to this, researchers trained a linear regression model which is named as readout that result in the designated trajectories known as output dynamics. These output dynamics is a resultant of exploiting the embedded innate trajectory.

Researchers say that this process can be applied to the other chaotic dynamical systems which are not limited to RNN in silicon since neither module nor hierarchical structures are required. Also, this embedding process is accomplished by modifying fewer parameters using the method of reservoir computing. The reservoir computing is an approach for making machine learning algorithms run faster, to expedite the computing process. Researches find this scheme to be more stable and less computationally expensive than conventional methods of back propagation to train the network parameters. Additionally, they added feedback classifier to the trained chaotic systems to autonomously generate specific symbolic systems.

Researchers suggest that two mechanisms are required for the successful designing of a CI model. The first is the differences among the trajectories are sufficiently enlarged through the temporal development to realize the stochastic symbol transition. A stochastic matrix is a square matrix that describes the transition of a Markov chain. A Markov chain is used to describe the sequence of possible events, where the probability of each event depends upon the state attained in the previous event. The second mechanism involves creating a spatiotemporal pattern to analyse the computing processes of the model. The Spatio-temporal pattern collects data across space and time and is utilized for by humans for solving multi-step problems by analysing the movement of objects in space and time.

Conclusion

Researchers say that this model will be helpful to understand the underlying mechanism of the brain's information processing from a certain perspective. Furthermore, as the high-dimensional chaos has the rich expressive capability to design CI, henceforth, this model will aid in understanding the mechanism of the contribution of the high-dimensional chaos to the information processing in animal brains.