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Filtering by: Creator Chung, Po-luen Remove constraint Creator: Chung, Po-luen Advisor Tumer, Kagan Remove constraint Advisor: Tumer, Kagan

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  • Distortion of Agent States for Improved Coordination

    Creator:
    Yates, Connor L
    Abstract:
    Many real world problems have partial solutions or intermediary steps which can lead toward solving the problem. When assembling robotic teams to solve these problems, we have intuition about which intermediary steps are more useful than others. We examine methods to identify and apply our designer intuition onto tightly coupled...
    Resource Type:
    Honors College Thesis
    Full Text:
    Robotics Dr. Geoff Hollinger, Committee Member, representing Robotics Dr. Jen Jen Chung, Committee

  • Providing Informative Feedback for Learning in Tightly Coupled Multiagent Domains

    Creator:
    Rockefeller, Golden C.
    Abstract:
    Autonomous agents that sense, decide, act, and coordinate effectively with each other are critical in many real-world domains such as autonomous driving, search and rescue missions, air traffic management, and underwater or deep space exploration. All such domains share a key difficulty: though high-level mission goals are clear to system...
    Resource Type:
    Dissertation

  • Counterfactual thinking applied to multiagent systems

    Creator:
    Zerbel, Nicholas
    Abstract:
    Multiagent approaches are well suited to designing autonomous solutions for systems that feature complex interactions between many individuals such as in autonomous traffic systems and multi-robot exploration systems. However, creating autonomous agents that function effectively in these systems is a challenging task. In these complex environments, agents need informative reward...
    Resource Type:
    Dissertation
    Full Text:
    on the performance of the team as a whole. Other reward shaping strategies like Po- tential Based

  • Robust Long Term Autonomy Through Local Behaviors

    Creator:
    Yates, Connor L.
    Abstract:
    Multi-robot teams offer promising solutions for many long term deployments in remote and dangerous domains, such as extraterrestrial or underseas exploration. However, long term deployments present many problems preventing robot teams from operating effectively. Learning over long time scales is makes it difficult to assign credit to robots' actions, as...
    Resource Type:
    Dissertation
    Full Text:
    robot is po- sitioned at the origin of the figure, with global heading θ. The goal is at position g, and