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Skip to Search Results- 50Artificial Intelligence
- 21Language
- 19Machine Learning
- 8Reinforcement Learning
- 6Deep Learning
- 5Natural Language Processing
- 1Akbari, Mojtaba
- 1Asadi Atui, Kavosh
- 1Ashley, Dylan R
- 1Ashrafi Asli, Seyed Arad
- 1Atrazhev, Peter
- 1Avni, Anoosha E.
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{Multi-Agent Deep Reinforcement Learning for Autonomous Energy Coordination in Demand Response Methods for Residential Distribution Networks
DownloadFall 2023
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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Spring 2015
This thesis examines the role that language plays in labor conflict. Nelson (2003: 449) argues that words are necessary for conflict: words initiate, maintain, elevate, defuse, and can resolve human conflict. My study follows Nelson in an exploration of how language was mobilized during the...
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Fall 2013
This dissertation examines four Indigenous novels published in Canada and the United States between 1990 and 2000. Building upon Indigenous and non-Indigenous theories of literary nationalism, cosmopolitanism, and globalization, this project focuses on narrative articulations of Indigenous...
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Using Social Media for Health Information: How New Technologies are Being Used in HIV/AIDS Communication
DownloadSpring 2014
Social networking sites, mobile technologies and other information and communications technologies have become popular ways of connecting. The health information field is no exception; however, what are best practices and strategies to effectively use the affordances of these tools, and currently...
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Fall 2015
Extensive-form games are a powerful framework for modeling sequential multi-agent interactions. In extensive-form games with imperfect information, Nash equilibria are generally used as a solution concept, but computing a Nash equilibrium can be intractable in large games. Instead, a variety of...
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Spring 2016
Game theoretic solution concepts, such as Nash equilibrium strategies that are optimal against worst case opponents, provide guidance in finding desirable autonomous agent behaviour. In particular, we wish to approximate solutions to complex, dynamic tasks, such as negotiation or bidding in...
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Spring 2020
Reinforcement learning (RL) is a powerful learning paradigm in which agents can learn to maximize sparse and delayed reward signals. Although RL has had many impressive successes in complex domains, learning can take hours, days, or even years of training data. A major challenge of contemporary...
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Fall 2020
This thesis is offered as a step forward in our understanding of forgetting in artificial neural networks. ANNs are a learning system loosely based on our understanding of the brain and are responsible for recent breakthroughs in artificial intelligence. However, they have been reported to be...
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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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Spring 2017
With the growing population of the elderly and the decline of population growth rate, developed countries are facing problems in taking care of their elderly. One of the issues that is becoming more severe is the issue of companionship for the aged people, particularly those who chose to live...