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Skip to Search Results- 49Artificial Intelligence
- 19Machine Learning
- 11Reinforcement Learning
- 6Game Theory
- 5Deep Learning
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- 1Akbari, Mojtaba
- 1Asadi Atui, Kavosh
- 1Ashley, Dylan R
- 1Ashrafi Asli, Seyed Arad
- 1Atrazhev, Peter
- 1Behboudian, Paniz
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Fall 2015
Brammadesam Manavalan, Yathirajan
Displaying believable emotional reactions in virtual characters is required in applications ranging from virtual-reality trainers to video games. Manual scripting is the most frequently used method and enables an arbitrarily high fidelity of the emotions displayed. However, scripting is labor...
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Spring 2016
Game theoretic solution concepts, such as Nash equilibrium strategies that are optimal against worst case opponents, provide guidance in finding desirable autonomous agent behaviour. In particular, we wish to approximate solutions to complex, dynamic tasks, such as negotiation or bidding in...
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Spring 2015
Computer-based interactive environments present a compelling platform for research in Artificial Intelligence. Using games as its domains, this work has traditionally focused on building AI agents that can play games well (e.g., Checkers, Go, or StarCraft). In more recent years, a parallel line...
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Fall 2016
The field of biomedicine is reeling from “information overload”. Indeed, biomedical researchers find it almost impossible to stay current with published literature due to the vast amounts of data being generated and published. As a result, they are turning to text mining. Over the past two...
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Spring 2020
Reinforcement learning (RL) is a powerful learning paradigm in which agents can learn to maximize sparse and delayed reward signals. Although RL has had many impressive successes in complex domains, learning can take hours, days, or even years of training data. A major challenge of contemporary...
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Fall 2015
Artificial Intelligence (AI) techniques have been widely used in video games to control non-playable characters. More recently, AI has been applied to automated story generation with the objective of managing the player’s experience in an interactive narrative. Such AI experience managers can...
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An Empirical Study on Learning and Improving the Search Objective for Unsupervised Paraphrasing
DownloadSpring 2022
Research in unsupervised text generation has been gaining attention over the years. One recent approach is local search towards a heuristically defined objective, which specifies language fluency, semantic meanings, and other task-specific attributes. Search in the sentence space is realized by...
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Fall 2023
Many works of art are created through the process of an artist sketching and then incrementally increasing the fidelity of the artwork. This requires significant amounts of work and effort throughout, but not all steps require the same amount of artistic input. Certain parts only require...
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Fall 2023
Krishna Guruvayur Sasikumar, Aakash
The application of reinforcement learning (RL) to the optimal control of building systems has gained traction in recent years as it can reduce building energy consumption and improve human comfort, without requiring the knowledge of the building model. However, existing RL solutions for building...
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Developing Hybrid Artificial Intelligence Model for Construction Labour Productivity Prediction and Optimization
DownloadFall 2021
Construction labour productivity (CLP) is considered one of the most important parameters affecting the performance of construction projects. Therefore, modeling CLP is a crucial step in construction projects. Accurate prediction of CLP helps in effective planning, cost estimating, and...