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- 1Changing Discrete Distributions
- 1Combinatorial game theory
Results for "supervisors_tesim:"Müller, Martin (Computing Science)""
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Spring 2020
In this thesis, we focus on finding efficient practical random sampling methods for time-changing discrete distributions. We empirically study ten methods including existing algorithms, and two new ones: three level search and the flat method. We review the core ideas of existing methods...
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Fall 2016
In this thesis, we investigate the move prediction problem in the game of Go by proposing a new ranking model named Factorization Bradley Terry (FBT) model. This new model considers the move prediction problem as group competitions while also taking the interaction between features into account....
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Fall 2022
Monte Carlo Tree Search (MCTS) is an extremely successful search-based frame- work for decision making. With an accurate simulator of the environment’s dynamics, it can achieve great performance in many games and non-games applications. However, without a perfect simulator, the performance...
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Spring 2016
This thesis applies the Monte Carlo Random Walk method (MRW) to motion planning. We explore different global and local restart strategies to improve the performance. Several new algorithms based on the MRW approach, such as bidirectional Arvand and optimizing planner Arvand*, are introduced and...
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Spring 2015
Temperature Discovery Search (TDS) is a forward search method for computing or approximating the temperature of a combinatorial game. Temperature and mean are important concepts in combinatorial game theory, which can be used to develop efficient algorithms for playing well in a sum of subgames....
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Spring 2023
Reinforcement learning (RL) defines a general computational problem where the learner must learn to make good decisions through interactive experience. To be effective in solving this problem, the learner must be able to explore the environment, make accurate predictions about the future, and...
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Fall 2023
In this thesis, we investigate applying deep learning techniques to learn the win-loss-draw results contained in the databases of the checkers-playing program CHINOOK. Our initial objectives were to (1) compare a deep-learning-based compression scheme versus the custom algorithm used in CHINOOK,...
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Spring 2016
This thesis proposes, analyzes and tests different exploration-based techniques in Greedy Best-First Search (GBFS) for satisficing planning. First, we show the potential of exploration-based techniques by combining GBFS and random walk exploration locally. We then conduct deep analysis on how...
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Spring 2020
Two-Player alternate-turn perfect-information zero-sum games have been suggested as a testbed for Artificial Intelligence research since Shannon in 1950s. In this thesis, we summarize and develop algorithms for this line of research. We focus on the game of Hex — a game created by Piet Hein in...