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Skip to Search Results- 85Artificial Intelligence
- 22Machine Learning
- 21Game theory
- 10Computer Games
- 10Reinforcement Learning
- 8Planning
- 4Müller, Martin
- 3Bowling, Michael
- 3Johanson, Michael
- 3Lanctot, Marc
- 3Mueller, Martin
- 3Zinkevich, Martin
- 65Graduate and Postdoctoral Studies (GPS), Faculty of
- 65Graduate and Postdoctoral Studies (GPS), Faculty of/Theses and Dissertations
- 21Computing Science, Department of
- 21Computing Science, Department of/Technical Reports (Computing Science)
- 4Toolkit for Grant Success
- 4WISEST Summer Research Program
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Spring 2010
This research focuses on developing AI agents that play arbitrary Atari 2600 console games without having any game-specific assumptions or prior knowledge. Two main approaches are considered: reinforcement learning based methods and search based methods. The RL-based methods use feature vectors...
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Spring 2015
Computer-based interactive environments present a compelling platform for research in Artificial Intelligence. Using games as its domains, this work has traditionally focused on building AI agents that can play games well (e.g., Checkers, Go, or StarCraft). In more recent years, a parallel line...
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2012
Lanctot, Marc, Gibson, Richard, Burch, Neil, Szafron, Duane
In large extensive form games with imperfect information, Counterfactual Regret Minimization (CFR) is a popular, iterative algorithm for computing approximate Nash equilibria. While the base algorithm performs a full tree traversal on each iteration, Monte Carlo CFR (MCCFR) reduces the per...
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Fall 2023
Self-play is a technique for machine learning in multi-agent systems where a learning algorithm learns by interacting with copies of itself. Self-play is useful for generating large quantities of data for learning, but has the drawback that agents the learner will face post-training may have...
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Fall 2019
Improvisation is a form of live theatre where artists perform real-time, dynamic problem solving to collaboratively generate interesting narratives. The main contribution of this thesis is the development of artificial improvisation: improvised theatre performed by humans alongside intelligent...
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Fall 2017
Modern board, card, and video games are challenging domains for AI research due to their complex game mechanics and large state and action spaces. For instance, in Hearthstone — a popular collectible card (CC) (video) game developed by Blizzard Entertainment — two players first construct their...
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Spring 2019
Current state-of-the-art algorithms for trick-taking card games use a process called determinization. Determinization is a technique that allows the application of perfect information state evaluation algorithms to imperfect information games. It involves a two-step process in which a perfect...
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2010
Mueller, Martin, Hoffman, Joerg, Nakhost, Hootan
Technical report TR10-02. A ubiquitous feature of planning problems -- problems involving the automatic generation of action sequences for attaining a given goal -- is the need to economize limited resources such as fuel or money. While heuristic search, mostly based on standard algorithms such...
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Interrelating Prediction and Control Objectives in Episodic Actor-Critic Reinforcement Learning
DownloadFall 2020
The reinforcement learning framework provides a simple way to study computational intelligence as the interaction between an agent and an environment. The goal of an agent is to accrue as much reward as possible by intelligently choosing actions given states. This problem of finding a policy that...
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Spring 2010
The Monte-Carlo Tree Search (MCTS) algorithm Upper Confidence bounds applied to Trees (UCT) has become extremely popular in computer games research. Because of the importance of this family of algorithms, a deeper understanding of when and how their different enhancements work is desirable. To...