BUILDING AN ARTIFICIAL CEREBELLUM USING A SYSTEM OF DISTRIBUTED Q-LEARNING AGENTS

Abstract

About 400 million years ago sharks developed a separate co-processor in their brains that not only made them faster but also more precisely coordinated. This co-processor, which is nowadays called cerebellum, allowed sharks to outperform their peers and survive as one of the fittest. For the last 40 years or so, researchers have been attempting to provide robots and other machines with this type of capability. This thesis discusses currently used methods to create artificial cerebellums and points out two main shortcomings: 1) framework usability issues and 2) building blocks incompatibility issues. This research argues that the framework usability issues hinder the production of good quality artificial cerebellums for a large number of applications. Furthermore, this study argues that the building blocks incompatibility issues make artificial cerebellums less efficient that they could be, given our current technology. To tackle the framework usability issues, this thesis research proposes the use of a new framework, which formalizes the task of creating artificial cerebellums and offers a list of simple steps to accomplish this task. Furthermore, to tackle the building blocks incompatibility issues, this research proposes thinking of artificial cerebellums as a set of cooperating q-learning agents, which utilize a new technique called Moving Prototypes to make better use of the available memory and computational resources. Furthermore, this work describes a set of general guidelines that can be applied to accelerate the training of this type of system. Simulation is used to show examples of the performance improvements resulting from the use of these guidelines. To illustrate the theory developed in this dissertation, this paper implements a cerebellum for a real life application, namely, a cerebellum capable of controlling a type of mining equipment called front-end loader. Finally, this thesis proposes the creation of a development tool based on this formalization. This research argues that such a development tool would allow engineers, scientists and technicians to quickly build customized cerebellums for a wide range of applications without the need of becoming experts on the area of Artificial Intelligence, Neuroscience or Machine Learning.