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Skip to Search Results- 49Artificial Intelligence
- 19Machine Learning
- 19Planning
- 12Reinforcement Learning
- 6Heuristic Search
- 5Deep Learning
- 1Akbari, Mojtaba
- 1Asadi Atui, Kavosh
- 1Ashley, Dylan R
- 1Ashrafi Asli, Seyed Arad
- 1Atrazhev, Peter
- 1Behboudian, Paniz
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Spring 2023
With machine learning models becoming more complicated and more widely applied to solve real-world challenges, there comes the need to explain their reasoning. In parallel with the advancements of deep learning methods, Explainable AI (XAI) algorithms have been proposed to address the issue of...
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Motion Planning of Robotic Systems in Diagnostic and Therapy Applications Using Control and AI
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This thesis presents significant research on robotic motion planning within diagnostic and therapy applications, with a primary focus on the integration of control and AI techniques. The research encompasses three main contributions: a robotic ultrasound imaging method, a robot-assisted...
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Development of AI-based ergonomics risk assessment tools for harmonization of industrial work systems
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Manufacturing industry workers face significant ergonomic risks due to poorly designed work systems. Consequently, it is crucial to periodically assess work systems to identify areas for improvement. However, the assessment process is often disregarded due to the absence of userfriendly...
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{Multi-Agent Deep Reinforcement Learning for Autonomous Energy Coordination in Demand Response Methods for Residential Distribution Networks
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In the field of collaborative learning and decision-making, this thesis aims to explore the effects of individual and joint rewards on the performance and coordination of agents in complex environments. The research objectives encompass two main aspects: firstly, to determine the objective...
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Enhancements to Methods for Planning and Scheduling Fabrication Projects Utilizing Multiskilled Labour Resources
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In prefabrication and off-site construction, various multiskilled work crews need to be assembled to work on different workstations to process custom-designed work units, each having specific requirements for material handling, assembly connections, welding, etc. However, the frequent labour...
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On Efficient Planning in Large Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning
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A practical challenge in reinforcement learning is large action spaces that make planning computationally demanding. For example, in cooperative multi-agent reinforcement learning, a potentially large number of agents jointly optimize a global reward function, which leads to a blow-up in the...
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Fall 2023
The increasing popularity of Deep Neural Networks (DNN) has led to their application to many domains, including Music Generation. However, these large DNN-based models are heavily dependent on their training dataset, which means they perform poorly on musical genres that are out-of-distribution...
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Data-Driven and Artificial Intelligence Approach to Dynamic Truck Fleet Dispatching and Shovel Allocation Planning in Open-Pit Mines
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An open-pit mine is a highly dynamic environment where different equipment resources are allocated to mining areas to extract metal-bearing rock and waste, for pit development, following a set flow of activities. The material mined is then transported through the mine road network to different...
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Spring 2023
AlphaZero is a self-play reinforcement learning algorithm that achieves superhuman play in the games of chess, shogi, and Go via policy iteration. To be an effective policy improvement operator, AlphaZero’s search needs to have accurate value estimates for the states that appear in its search...
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Fall 2023
Many works of art are created through the process of an artist sketching and then incrementally increasing the fidelity of the artwork. This requires significant amounts of work and effort throughout, but not all steps require the same amount of artistic input. Certain parts only require...