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Skip to Search Results- 1Ameen, Saqib
- 1Farhad Haqiqat
- 1Gao, Chao
- 1Huntley, Daniel A
- 1Long, Jeffrey Richard
- 1Rayner, David Christopher Ferguson
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Fall 2018
Domain-specific knowledge plays a significant role in the success of many Monte Carlo Tree Search (MCTS) programs. The details of how knowledge affects MCTS are still not well understood. In this thesis, we focus on identifying the effects of different types of knowledge on the behaviour of the...
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Fall 2019
Creating strong AI systems for trick-taking card games is challenging. This is mostly due to the long action sequences and extremely large information sets common in this type of game. Thus far, search-based methods have shown to be most effective in this domain. In this thesis, I explore...
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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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Spring 2015
Rayner, David Christopher Ferguson
Heuristic search is a central problem in artificial intelligence. Among its defining properties is the use of a heuristic, a scalar function mapping pairs of states to an estimate of the actual distance between them. Accurate heuristics are generally correlated with faster query resolution and...
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Spring 2012
Recent real-time heuristic search algorithms have demonstrated outstanding performance in video game pathfinding. However, their applications have been thus far limited to that domain. We proceed with the aim of facilitating wider applications of real-time search by fostering a greater...
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Spring 2023
Cost-guided bottom-up search (BUS) algorithms use a cost function to guide the search for solving program synthesis tasks. In this thesis, we show that current state-of-the-art cost-guided BUS algorithms suffer from a common problem: they can lose useful information given by the model and fail to...
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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...
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Fall 2011
In this dissertation we discuss problems of search, inference and opponent modelling in imperfect information games in the context of creating a computer player for the popular german card game skat. In so doing, we demonstrate three major contributions to the field of artificial intelligence...
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Fall 2020
We explore the interplay of generate-and-test and gradient-descent techniques for solving online supervised learning problems. The task in supervised learning is to learn a function using samples of inputs to output pairs. This function is called the target function. The standard way to learn...