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- 22Machine Learning
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- 2Johanson, Michael
- 2Pelletier, Francis J.
- 1Adams, Cathy
- 1Ady, Nadia M.
- 49Graduate and Postdoctoral Studies (GPS), Faculty of
- 49Graduate and Postdoctoral Studies (GPS), Faculty of /Theses and Dissertations
- 18Computing Science, Department of
- 18Computing Science, Department of/Technical Reports (Computing Science)
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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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1991
Pelletier, Francis J., Schubert, Lenhart
Introduction: This very short book is apparently intended as a supplementary text in a graduate AI course. The author describes it as a \"text and reference work on the applications of non-standard logics to artificial intelligence (AI).\" It gives short and concise (too short and too concise, in...
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[Review of the book Formal Methods in Artificial Intelligence, by Aamsay]
1996
Introduction: Many universities teach artificial intelligence (AI) by having one undergraduate course that introduces students to a very wide variety of topics, usually including search and search heuristics, representational systems (including formal logic), problem solving, vision, expert...
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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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2019-10-01
SSHRC IG awarded 2020: The global economy is on the verge of a profound transformation as artificial intelligence (AI) achieves and exceeds human-level abilities in a growing number of domains. Canada is already a world leader in the development and commercialization of AI technologies. However,...
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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...