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    Teaching robots about human environments: Leveraging human interaction to efficiently learn and use multisensory object affordances 

    Chu, Vivian (Georgia Institute of Technology, 2018-01-09)
    The real world is complex, unstructured, and contains high levels of uncertainty. Although past work shows that robots can successfully operate in situations where a single skill is needed, they will need a framework that ...
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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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    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 ...

    Deterministically initialized localized learning networks 

    Miller, Mark Todd (Georgia Institute of Technology, 1991)
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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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    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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    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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    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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    Human-guided task transfer for interactive robots 

    Fitzgerald, Tesca Kate (Georgia Institute of Technology, 2020-07-06)
    Adaptability is an essential skill in human cognition, enabling us to draw from our extensive, life-long experiences with various objects and tasks in order to address novel problems. To date, robots do not have this kind ...
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    EvalAI: Evaluating AI systems at scale 

    Deshraj (Georgia Institute of Technology, 2018-12-06)
    Artificial Intelligence research has progressed tremendously in the last few years. There has been the introduction of several new multi-modal datasets and tasks due to which it is becoming much harder to compare new ...
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    AuthorBalasubramanian, Krishnakumar (1)Balch, Tucker (1)Betancourt, David (1)Bhatta, Sambasiva R. (1)Boone, Gary Noel (1)Chattopadhyay, Prithvijit (1)Chu, Vivian (1)Cox, Michael Thomas (1)Davies, Jim (1)dePalma, Nicholas Brian (1)... View MoreSubject
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    Machine learning (19)Analogy (6)Problem solving (5)Reinforcement learning (5)Computer vision (4)Deep learning (4)Robotics (4)Cognitive science (3)Reasoning (3)... View MoreDate Issued2020 - 2021 (4)2010 - 2019 (19)2000 - 2009 (8)1990 - 1999 (10)1989 - 1989 (1)Has File(s)Yes (42)
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