Optimization of dynamical systems towards criticality and intelligent behavior

Abstract

With the progress of computing power, artificial intelligence (AI) systems are able to achieve outstanding results that surpass human-level performance on some tasks, such as image recognition. Mainstream AI systems are only able to learn one or more specific tasks, and they commonly fail to go beyond what they were trained for. To have a real breakthrough in the field of AI, those systems require adaptability and the capacity to learn general tasks and data distributions. Such characteristics are among the objectives of artificial general intelligence (AGI) research. The inspiration from biology, neuroscience, and complex systems may guide the development of AGI because AI systems are still rigid instead of self-organizing. Therefore, the research reported in this thesis aims at intelligent dynamical systems that resemble the brain, such as the brain’s critical behavior that may allow the cortex to self-organize to criticality in order to increase computational capacity. The plan to accomplishsuch systems encompasses the usage of optimization methods to find adequate interactions of the components in a complex system, how they are connected with each other, and how they adapt those interactions and connections overtime. The dynamical systems investigated in this research are cellular automata, Boolean networks, and recurrent neural networks with abstract neuron models or with more biologically plausible ones (spiking neurons). Themain optimization method applied in these systems is evolutionary computa-tion or artificial evolution. There are three parts in the presented research. Thefirst uses a deep neural network library to evolve dynamical systems towardscriticality. The second part works on the complexification of spiking neuralnetworks with adaptive synapses for solving tasks in mutable environments.The last one involves the application of neural cellular automata for control-ling robots and even developing their morphology with artificial embryogeny.The results of this research attempt to address some unanswered questionsrelated to the practicality, methodology, and benefits of applying dynamicalsystems in AI.