Theses and Dissertations from UMD

Permanent URI for this communityhttp://hdl.handle.net/1903/2

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM

More information is available at Theses and Dissertations at University of Maryland Libraries.

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    A Method For Improving Decentralized Task Allocation For Multiagent Systems in Low-Communication Environments.
    (2021) Akoroda, Oghenetekevwe; Herrmann, Jeffrey W; Systems Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Communication is an important aspect of task allocation, but it has a cost and low communication restricts the information exchange needed for task allocation. As a result, a lot of decentralized task allocation algorithms perform worse as communication worsens. The contribution of this thesis is a method to improve the performance of a task allocation algorithm in low-communication environments and reduce the cost of communication by restricting communication. This method, applied to the Consensus Based Auction Algorithm (CBAA), determines when an agent should communicate and estimates the information that will be received from other agents. This method is compared to other decentralized task allocation algorithms at different levels of communication in a ship protection scenario. Results show that this method when applied to CBAA performs comparably to CBAA while reducing communication.
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    DECENTRALIZED MULTIAGENT METAREASONING APPLICATIONS IN TASK ALLOCATION AND PATH FINDING
    (2021) Langlois, Samuel; Herrmann, Jeffrey W.; Systems Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Decentralized task allocation and path finding are two problems for multiagent systems where no single fixed algorithm provides the best solution in all environments. Past research has considered metareasoning approaches to these problems that take in map, multiagent system, or communication information. None of these papers address the application of metareasoning about individual agent state features which could decrease communication and increase performance for decentralized systems. This thesis presents the application of a meta-level policy that is conducted offline using supervised learning through extreme gradient boosting. The multiagent system used here operates under full communication, and the system uses an independent multiagent metareasoning structure. This thesis describes research that developed and evaluated metareasoning approaches for the multiagent task allocation problem and the multiagent path finding problem. For task allocation, the metareasoning policy determines when to run a task allocation algorithm. For multiagent path finding, the metareasoning policy determines which algorithm an agent should use. The results of this comparative research suggest that this metareasoning approach can reduce communication and computational overhead without sacrificing performance.