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Learnable Knowledge For Autonomous Agents, Saminda Abeyruwan
Learnable Knowledge For Autonomous Agents, Saminda Abeyruwan
Open Access Dissertations
While computation power has increased and the statistical machine learning methods have made substantial advancement, many problems that would benefit from real-time interpretation have not exploited their combined strengths. For instance, the problem of gathering data from the environment and transforming it into knowledge as well as updating the knowledge as new data become available. Currently, with substantial expressivity and moderate computational cost, high-level languages or first-order predicate logic or model-based machine learning are used for static representation of knowledge, that is used for reasoning and inferring. In this dissertation, we address how an entity dynamically gather knowledge from environmental …
Synchronous Control Of A Reinforcement Learning Based Brain-Machine Interface With Biological Feedback, Noeline W. J. A. L. Prins
Synchronous Control Of A Reinforcement Learning Based Brain-Machine Interface With Biological Feedback, Noeline W. J. A. L. Prins
Open Access Dissertations
Brain-Machine Interfaces (BMIs) have the potential of restoring functionality of persons suffering from paralysis and amputations. At present, BMIs have been developed to use cortical neural signals and control prosthetic devices or to stimulate paralyzed limbs. However, these BMIs rely on an external training signal (usually desired kinematics) as a reference to infer an error signal to be able to adapt the decoder appropriately and learn the task. For amputees and paralyzed persons, a desired kinematic cannot be measured directly. We propose to acquire an error or reward signal from the brain itself as a training signal for motor decoders. …