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Filtering by: Creator Liu, Z Remove constraint Creator: Liu, Z Degree Field Mechanical Engineering Remove constraint Degree Field: Mechanical Engineering Subject Multiagent systems Remove constraint Subject: Multiagent systems

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  • Combining coordination mechanisms to improve performance in multi-robot teams

    Creator:
    Nasroullahi, Ehsan
    Abstract:
    Coordination is essential to achieving good performance in cooperative multiagent systems. To date, most work has focused on either implicit or explicit coordination mechanisms, while relatively little work has focused on the benefits of combining these two approaches. In this work we demonstrate that combining explicit and implicit mechanisms can...
    Resource Type:
    Masters Thesis
    Full Text:
    ]) between the agent policy evaluation function gi and system objective G 21 at state z, as: Fgi � ° z

  • Design of Complex Engineered Systems Using Multiagent Coordination

    Creator:
    Soria Zurita, Nicolás Francisco
    Abstract:
    This thesis is the combination of two research publications working toward a unified strategy in which the design of complex engineered systems can be completed using a multiagent coordination approach. Current engineered system modeling techniques segment large complex models into multiple groups to be simulated independently. These methods restrict the...
    Resource Type:
    Masters Thesis
    Full Text:
    difference evaluation function is defined as: Di(z) = G(z)−G(z−i + ci) (3.3) where z is the overall system

  • A multiagent approach to identifying innovation in design components

    Creator:
    Rebhuhn, Carrie M.
    Abstract:
    Innovation is a key element for a product to achieve market success, but identifying it within product or even defining the term is a difficult task. Identifying innovation has been approached in many different ways. Experts in design engineering may identify innovative designs based on an analysis of a product's...
    Resource Type:
    Masters Thesis
    Full Text:
    : Di(z) = G(z) − G(z−i + ci) (2.2) where G(z) is the reward given to the entire system and G(z−i + ci

  • CLEAN learning to improve coordination and scalability in multiagent systems

    Creator:
    HolmesParker, Chris
    Abstract:
    Recent advances in multiagent learning have led to exciting new capabilities spanning fields as diverse as planetary exploration, air traffic control, military reconnaissance, and airport security. Such algorithms provide a tangible benefit over traditional control algorithms in that they allow fast responses, adapt to dynamic environments, and generally scale well....
    Resource Type:
    Dissertation
    Full Text:
    , in most domains, the local rewards are not aligned with the system reward G(z). In such domains, an

  • A neuro-evolutionary multiagent approach to multi-linked inverted pendulum control

    Creator:
    Sills, Stephen
    Abstract:
    Recent work has shown humanoid robots with spinal columns, instead of rigid torsos, benefit from both better balance and an increased ability to absorb external impact. Similarly, snake robots have shown promise as a viable option for exploration in confined spaces with limited human access, such as during power plant...
    Resource Type:
    Masters Thesis

  • Multi-Objective Optimization in Multiagent Systems

    Creator:
    Yliniemi, Logan Michael
    Abstract:
    Cooperative multiagent systems are used as solution concepts in many application domains including air traffic control, satellite communications, and extra planetary exploration. As systems become more distributed and complex, we observe three phenomena. First, these systems cannot be accurately modeled, rendering traditional model based control methods inadequate. Second, system parameters...
    Resource Type:
    Dissertation
    Full Text:
    global system performance for the system considering the joint state- action z, and Gpz´iq is Gpzq for a

  • Theoretical and implementation improvements for difference evaluation functions

    Creator:
    Colby, Mitchell Kataichi
    Abstract:
    Multiagent learning with cooperative coevolutionary algorithms is a critical area of research, and is relevant to many real-world applications including air traffic control, distributed sensor network control, and game-theoretic applications such as border patrol. A key difficulty in multiagent learning is the credit assignment problem, where the impact of each...
    Resource Type:
    Dissertation