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Filtering by: Creator Schram, Julie B. Remove constraint Creator: Schram, Julie B. Committee Member Hollinger, Geoff Remove constraint Committee Member: Hollinger, Geoff

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  • Human Performance with Multiple UAV Systems: Use Cases and Modeling

    Creator:
    Uriarte, Patrick A.
    Abstract:
    Commercial and public safety usage of Unmanned Aerial Vehicles in the National Airspace is currently restricted by federal regulation. The Federal Aeronautics Association is interested in modifying the restrictions; however, research is needed to study the human factor and required aptitude for a single human operating multiple UAVs. This Master’s...
    Resource Type:
    Capstone Project
    Full Text:
    APPENDIX B. LOOSELY COUPLED USE CASE: DELIVERY UAVS. ................................. 65 B.1. NOMINAL USE

  • Coordination for Scalable Multiple Robot Planning Under Temporal Uncertainty

    Creator:
    Marcon dos Santos, Gilberto
    Abstract:
    This dissertation incorporates coalition formation and probabilistic planning towards a domain-independent automated planning solution scalable to multiple heterogeneous robots in complex domains. The first research direction investigates the effectiveness of Task Fusion and introduces heuristics that improve task allocation and result in better quality plans, while requiring lower computational cost...
    Resource Type:
    Dissertation
    Full Text:
    : Coordination for Scalable Multiple Robot Planning Under Temporal Uncertainty Abstract approved: Julie A

  • Artificial Swarm Control Through Learning Based Leadership

    Creator:
    Tuzel, Ovunc
    Abstract:
    Collective migration in animal groups is often guided by distributed leader agents that influence their peers by modulating their motion behaviors. Distributed leadership is a promising navigation strategy for artificial swarms, but designing the leaders’ controllers is difficult due to the swarm’s collective emergent behaviors. The presented research formulates a...
    Resource Type:
    Masters Thesis
    Full Text:
    Based Leadership Abstract approved: Julie A. Adams Collective migration in animal groups is often

  • Learning in Complex Domains : Leveraging Multiple Rewards through Alignment

    Creator:
    Klinkhammer, Eric R.
    Abstract:
    In this thesis, we introduce alignment-based algorithms for improving the performance of reinforcement learning solutions for problems where the reward signal cannot be collapsed into a single number. Many real world problems require an agent to balance performance, longevity, and safety, and do so across different timelines. The key to...
    Resource Type:
    Masters Thesis

  • 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:
    joint-state-action. In (b) CFL introduces a counterfactual state where the left box has a vacuum robot

  • 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:
    further node can be observed. This is particularly important in configuration (b) where the global fitness

  • 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

  • D₊₊ : Structural Credit Assignment in Tightly Coupled Multiagent Domains

    Creator:
    Rahmattalabi, Aida
    Abstract:
    Autonomous multiagent teams can be used in complex exploration tasks to both expedite the exploration and improve the efficiency. However, use of multiagent systems presents additional challenges. Specifically, in domains where the agents' actions are tightly coupled, coordinating multiple agents to achieve cooperative behavior at the group level is difficult....
    Resource Type:
    Masters Thesis