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    A computational framework for unsupervised analysis of everyday human activities 

    Hamid, Muhammad Raffay (Georgia Institute of Technology, 2008-07-07)
    In order to make computers proactive and assistive, we must enable them to perceive, learn, and predict what is happening in their surroundings. This presents us with the challenge of formalizing computational models of ...
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    Troubleshooting physical devices : learning and instruction 

    Kolodner, Janet Lynne (Georgia Institute of Technology, 1990)
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    Novel document representations based on labels and sequential information 

    Kim, Seungyeon (Georgia Institute of Technology, 2015-07-23)
    A wide variety of text analysis applications are based on statistical machine learning techniques. The success of those applications is critically affected by how we represent a document. Learning an efficient document ...
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    Fractal reasoning 

    McGreggor, Brian Keith (Georgia Institute of Technology, 2013-11-18)
    Humans are experts at understanding what they see. Similarity and analogy play a significant role in making sense of the visual world by forming analogies to similar images encountered previously. Yet, while these acts ...
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    The role of experience in natural problem solving 

    Kolodner, Janet Lynne (Georgia Institute of Technology, 1986)
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    Construction and adaptation of AI behaviors in computer games 

    Mehta, Manish (Georgia Institute of Technology, 2011-08-19)
    Computer games are an increasingly popular application for Artificial Intelligence (AI) research, and conversely AI is an increasingly popular selling point for commercial digital games. AI for non playing characters (NPC) ...
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    Deterministically initialized localized learning networks 

    Miller, Mark Todd (Georgia Institute of Technology, 1991)
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    Manipulating state space distributions for sample-efficient imitation-learning 

    Schroecker, Yannick Karl Daniel (Georgia Institute of Technology, 2020-03-16)
    Imitation learning has emerged as one of the most effective approaches to train agents to act intelligently in unstructured and unknown domains. On its own or in combination with reinforcement learning, it enables agents ...
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    Establishing a Data Science 101 Pedagogy: Reimagining the MOOC Learning Experience Through a Case-Based Learning Methodology 

    Isom, Aaron F. (Georgia Institute of Technology, 2019-04)
    This work involved a comparative analysis of randomly selected Data Science Massive Open Online Courses (MOOCs) and master’s degree programs in investigating how effectively interdisciplinary curricula approaches were being ...
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    Evaluating visual conversational agents via cooperative human-AI games 

    Chattopadhyay, Prithvijit (Georgia Institute of Technology, 2019-04-26)
    As AI continues to advance, human-AI teams are inevitable. However, progress in AI is routinely measured in isolation, without a human in the loop. It is crucial to benchmark progress in AI, not just in isolation, but ...
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    AuthorMurdock, J. William (3)Stroulia, Eleni (3)Goel, Ashok K. (2)Gregori, Eric (2)Kolodner, Janet Lynne (2)Mahesh, Kavi (2)Ram, Ashwin (2)Ali, Khaled Subhi (1)Amundsen, Thomas Charles (1)Arkin, Ronald C. (1)... View MoreSubject
    Artificial intelligence (71)
    Machine learning (28)Reinforcement learning (8)Problem solving (7)Analogy (6)Robotics (6)Computer vision (4)Case-based reasoning (3)Cognitive science (3)Deep learning (3)... View MoreDate Issued2010 - 2020 (32)2000 - 2009 (15)1990 - 1999 (19)1986 - 1989 (3)
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