Gaining Perspective with an Evolutionary Cognitive Architecture for Intelligent Agents
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This thesis targets the boundary surrounding the creation of strong AI using AutoML (Automatic Machine Learning) through the development of a general cognitive architecture called Brain Evolver. To do this, the notion of what intelligence is in the context of machines and how it can practically be applied to physical intelligent agents is explored. Some core components that make up what a potentially strong AI system must possess are identified and outlined as basic task completion, exploration, scalability, noise reduction, generalization, memory, and credit-assignment. A wide set of tests that target these components are used to test the general capabilities of Brain Evolver as well as some more high-level tests that abstractly simulate space rover mission tasks. The notion of perspective and how it pertains to solving problems using appropriate levels of generalisation and historical information without explicitly storing all memory is also a subtle focus. Brain Evolver was developed using hypothetical reasoning from the literature reviewed and uses a modular design. All modules are implemented with evolutionary approaches and include Deep Neural Evolution, Relevance Estimation and Value Calibration of Evolutionary Algorithm Parameters, Meta Learning Shared Hierarchies, Attention, Spiking Neural Networks, and Guided Epsilon Exploration (a novel method). The relevance of these components in different combinations are analysed in the varying contexts of each test environment in order to gain insights and contribute to the body of evolutionary research targeted towards general problem solvers. The predictions made regarding the effect each module would have on each type of task proved to be unreliable and the program struggled with efficiency. However, Brain Evolver was still able to successfully and adequately solve all but one of the test environments in a completely autonomous way.