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    Single-tree GMM training 

    Curtin, Ryan R. (Georgia Institute of Technology, 2015-05-27)
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    Behavioral Diversity in Learning Robot Teams 

    Balch, Tucker (Georgia Institute of Technology, 1998)
    This work investigates the origins of behavioral diversity in learning robot teams. Behavioral diversity refers to the extent to which agents assume distinct behavioral roles in a group. Most research in multi-robot teams ...
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    Learning Roles: Behavioral Diversity in Robot Teams 

    Balch, Tucker (Georgia Institute of Technology, 1997)
    This paper describes research investigating behavioral specialization in learning robot teams. Each agent is provided a common set of skills (motor schema-based behavioral assemblages) from which it builds a task-achie ...
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    Combinational machine learning creativity 

    Guzdial, Matthew James (Georgia Institute of Technology, 2019-07-24)
    Computational creativity is a field focused on the study and development of behaviors in computers an observer would deem creative. Traditionally, it has relied upon rules-based and search-based artificial intelligence. ...
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    Innovative mining, processing, and application of big graphs 

    Zhou, Yang (Georgia Institute of Technology, 2017-03-22)
    With continued advances in science and technology, big graph (or network) data, such as World Wide Web, social networks, academic collaboration networks, transportation networks, telecommunication networks, biological ...
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    Constructing and evaluating executable models of collective behavior 

    Hrolenok, Brian Paul (Georgia Institute of Technology, 2018-10-19)
    Multiagent simulation (MAS) can be a valuable tool for biologists and ethologists studying collective animal behavior. However, constructing models for simulation is often a time-consuming manual task. Current state-of-the-art ...
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    Disease progression modeling using multi-dimensional continuous-time hidden Markov model 

    Liu, Yu-Ying (Georgia Institute of Technology, 2015-08-25)
    Continuous-Time Hidden Markov Model (CT-HMM) is a useful tool for modeling disease progression from noisy observed data arriving irregularly in time. However, the lack of any widely-accepted efficient learning algorithm ...
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    Integrating reinforcement learning into a programming language 

    Simpkins, Christopher Lee (Georgia Institute of Technology, 2017-06-26)
    Reinforcement learning is a promising solution to the intelligent agent problem, namely, given the state of the world, which action should an agent take to maximize goal attainment. However, reinforcement learning algorithms ...
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    Automated iterative game design 

    Zook, Alexander (Georgia Institute of Technology, 2016-12-06)
    Computational systems to model aspects of iterative game design were proposed, encompassing: game generation, sampling behaviors in a game, analyzing game behaviors for patterns, and iteratively altering a game design. ...
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    AI-infused security: Robust defense by bridging theory and practice 

    Chen, Shang-Tse (Georgia Institute of Technology, 2019-09-20)
    While Artificial Intelligence (AI) has tremendous potential as a defense against real-world cybersecurity threats, understanding the capabilities and robustness of AI remains a fundamental challenge. This dissertation ...
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    AuthorBalch, Tucker (3)Abowd, Gregory D. (2)Arkin, Ronald C. (2)Balasubramanian, Krishnakumar (2)Endo, Yoichiro (2)Essa, Irfan (2)Murdock, J. William (2)Ram, Ashwin (2)Reyes, Gabriel (2)Thomaz, Edison (2)... View MoreSubject
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    Artificial intelligence (29)Deep learning (15)Computer vision (13)Reinforcement learning (12)Robotics (9)Neural networks (8)Algorithms (7)Optimization (6)Data science (5)... View MoreDate Issued2020 - 2023 (24)2010 - 2019 (75)2000 - 2009 (13)1992 - 1999 (8)Has File(s)Yes (120)
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