Fundamental aspects of biologically-inspired regulatory mechanisms are considered in a robotics context, using artificial neural-network control systems . Regulatory mechanisms are used to control expression of genes, adaptation of form and behavior in organisms. Traditional neural network control architectures assume networks of neurons are fixed and are interconnected by wires. However, these architectures tend to be specified by a designer and are faced with several limitations that reduce scalability and tractability for tasks with larger search spaces. Traditional methods used to overcome these limitations with fixed network topologies are to provide more supervision by a designer. More supervision as shown does not guarantee improvement during training particularly when making incorrect assumptions for little known task domains. Biological organisms often do not require such external intervention (more supervision) and have self-organized through adaptation. Artificial neural tissues (ANT) addresses limitations with current neural-network architectures by modeling both wired interactions between neurons and wireless interactions through use of chemical diffusion fields. An evolutionary (Darwinian) selection process is used to ‘breed’ ANT controllers for a task at hand and the framework facilitates emergence of creative solutions since only a system goal function and a generic set of basis behaviours need be defined. Regulatory mechanisms are formed dynamically within ANT through superpositioning of chemical diffusion fields from multiple sources and are used to select neuronal groups. Regulation drives competition and cooperation among neuronal groups and results in areas of specialization forming within the tissue. These regulatory mechanisms are also shown to increase tractability without requiring more supervision using a new statistical theory developed to predict performance characteristics of fixed network topologies. Simulations also confirm the significance of regulatory mechanisms in solving certain tasks found intractable for fixed network topologies. The framework also shows general improvement in training performance against existing fixed-topology neural network controllers for several robotic and control tasks. ANT controllers evolved in a low-fidelity simulation environment have been demonstrated for a number of tasks on hardware using groups of mobile robots and have given insight into self-organizing system. Evidence of sparse activity and use of decentralized, distributed functionality within ANT controller solutions are found consistent with observations from neurobiology.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/19318 |
| Date | 05 March 2010 |
| Creators | Thangavelautham, Jekanthan |
| Contributors | Gabriele, D'Eleuterio |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
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