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Skip to Search Results- 2Abdi Oskouie, Mina
- 2Birkbeck, Neil Aylon Charles
- 2Cai, Zhipeng
- 2Chen, Jiyang
- 2Chowdhury, Md Solimul
- 2Chubak, Pirooz
- 74Machine Learning
- 70Reinforcement Learning
- 41Artificial Intelligence
- 36Machine learning
- 22Natural Language Processing
- 22Reinforcement learning
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Fall 2009
The creation of rich, immersive game worlds is one of the major goals for designers of modern story-based games. The inclusion of unique and interesting dialogues for all of a game's non-player characters (NPCs), especially the secondary NPCs, does a great deal to increase the believability of...
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Fall 2021
We study the problem of set discovery where given a few example tuples of a desired set, we want to find the set in a collection of sets. A challenge is that the example tuples may not uniquely identify a set, and a large number of candidate sets may be returned. Our focus is on interactive...
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Spring 2011
Physical simulations are in general very computationally intensive and required large and costly computing resources. Most of those simulations are rarely interactive as the link between visualization, interaction, and simulation is too slow. The recent development of parallel Graphic Processing...
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Fall 2011
In many cloud computing environments (e.g., Amazon’s public Elastic Computing Cloud and Openstack for private clouds), virtual machine (VM) instances are the unit of resource allocation. When possible, VM instances can be allocated on the same physical server and many techniques (e.g., using...
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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...