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Skip to Search Results- 2Abdi Oskouie, Mina
- 2Birkbeck, Neil Aylon Charles
- 2Cai, Zhipeng
- 2Chen, Jiyang
- 2Chowdhury, Md Solimul
- 2Chubak, Pirooz
- 83Machine Learning
- 76Reinforcement Learning
- 42Artificial Intelligence
- 37Machine learning
- 24Natural Language Processing
- 23reinforcement learning
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Fall 2014
Designing competitive Artificial Intelligence (AI) systems for Real-Time Strategy (RTS) games often requires a large amount of expert knowledge (resulting in hard-coded rules for the AI system to follow). However, aspects of an RTS agent can be learned from human replay data. In this thesis, we...
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Spring 2015
In this thesis, I study the problem of Monte-Carlo Planning in deterministic do- mains with sparse rewards. A popular algorithm in this suite, UCT, is studied. A new algorithm to incorporate state generalization in UCT using estimates of sim- ilar nodes and a distance metric is presented. The...
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Fall 2009
One way to create a champion level poker agent is to compute a Nash Equilibrium in an abstract version of the poker game. The resulting strategy is then used to play in the full game. With this approach, translation is required between the full and abstract games in order to use the...
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Static detection and identification of X86 malicious executables: A multidisciplinary approach
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In this thesis, we propose a novel approach to detect malicious executables in the network layer using a combination of techniques from bioinformatics, data mining and information retrieval. This approach requires translating malicious code into genome-like representations. Based on their...
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Spring 2019
Software Product Line Engineering (SPLE) creates configurable platforms that can be used to efficiently produce similar, and yet different, product variants. To implement SPLs, multiple variability implementation mechanisms have been suggested, including polymorphism. In this thesis, we talk...
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Spring 2012
We study linear estimation based on perturbed data when performance is measured by a matrix norm of the expected residual error, in particular, the case in which there are many unknowns, but the “best” estimator is sparse, or has small L1-norm. We propose a Lasso-like procedure that finds the...
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Statistically Significant Dependencies for Spatial Co-location Pattern Mining and Classification Association Rule Discovery
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Spatial co-location pattern mining and classification association rule discovery are two canonical tasks studied in the data mining community. Both of them focus on the detection of sets of features that show associations. The difference is that in spatial co-location pattern mining, the features...
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Spring 2014
Spatial interaction pattern mining is the process of discovering patterns that occur due to the interaction of Boolean features from a spatial domain. A positive interaction of a subset of features generates a co-location pattern, whereas a negative interaction of a subset of features generates a...