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Skip to Search Results- 21Game theory
- 6Poker
- 4Regret minimization
- 3Computer Games
- 3Machine learning
- 2Artificial Intelligence
- 3Johanson, Michael
- 2Bowling, Michael
- 2Lanctot, Marc
- 2Zinkevich, Martin
- 1Ajallooeian, Mohammad Mahdi
- 1Bernard, Benjamin
- 15Graduate and Postdoctoral Studies (GPS), Faculty of
- 15Graduate and Postdoctoral Studies (GPS), Faculty of/Theses and Dissertations
- 5Computing Science, Department of
- 5Computing Science, Department of/Technical Reports (Computing Science)
- 1Biological Sciences, Department of
- 1Biological Sciences, Department of/Journal Articles (Biological Sciences)
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Modelling phytoplankton across many scales: transient dynamics, human interactions, and niche differentiation in the light spectrum
DownloadFall 2021
In recent decades freshwater lakes have seen an increase in human presence. A common byproduct of this human presence is anthropogenic nutrient pollution resulting in eutrophication, a term that is becoming all too synonymous with harmful algal blooms. It is well known that phytoplankton...
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Monte Carlo Sampling and Regret Minimization for Equilibrium Computation and Decision-Making in Large Extensive Form Games
DownloadSpring 2013
In this thesis, we investigate the problem of decision-making in large two-player zero-sum games using Monte Carlo sampling and regret minimization methods. We demonstrate four major contributions. The first is Monte Carlo Counterfactual Regret Minimization (MCCFR): a generic family of...
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2009
Bowling, Michael, Zinkevich, Martin, Waugh, Kevin, Lanctot, Marc
Technical report TR09-15. Sequential decision-making with multiple agents and imperfect information is commonly modeled as an extensive game. One efficient method for computing Nash equilibria in large, zero-sum, imperfect information games is counterfactual regret minimization (CFR). In the...
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Optimal Mechanisms for Machine Learning: A Game-Theoretic Approach to Designing Machine Learning Competitions
DownloadSpring 2013
In this thesis we consider problems where a self-interested entity, called the principal, has private access to some data that she wishes to use to solve a prediction problem by outsourcing the development of the predictor to some other parties. Assuming the principal, who needs the machine...
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Regret Minimization in Games and the Development of Champion Multiplayer Computer Poker-Playing Agents
DownloadSpring 2014
Recently, poker has emerged as a popular domain for investigating decision problems under conditions of uncertainty. Unlike traditional games such as checkers and chess, poker exhibits imperfect information, varying utilities, and stochastic events. Because of these complications, decisions at...
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2007
Bowling, Michael, Johanson, Michael, Zinkevich, Martin, Piccione, Carmelo
Technical report TR07-14. Extensive games are a powerful model of multiagent decision-making scenarios with incomplete information. Finding a Nash equilibrium for very large instances of these games has received a great deal of recent attention. In this paper, we describe a new technique for...
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Sequential decision-making in a variable environment: modeling elk movement in Yellowstone National Park as a dynamic game.
Download2007
Potapov, A. B., Noonburg, E. G., Newman, L. A., Lewis, M. A., Crabtree, R. L.
We develop a suite of models with varying complexity to predict elk movement behavior during the winter on the Northern Range of Yellowstone National Park (YNP). The models range from a simple representation of optimal patch choice to a dynamic game, and we show how the underlying theory in each...
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Spring 2018
Decision-making problems with two agents can be modeled as two player games, and a Nash equilibrium is the basic solution concept describing good play in adversarial games. Computing this equilibrium solution for imperfect information games, where players have private, hidden information, is...