Saudi Cultural Missions Theses & Dissertations
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Item Restricted EFFECTIVE TASK ALLOCATION FOR AD HOC HUMAN-AGENT TEAMS(The University of Tulsa, 2024-05-11) Abuhaimed, Sami; Sen, SandipDue to the rapid advancement in autonomous agent capabilities, mixed human-agent teams will be increasingly deployed for new applications in our personal as well as professional spheres. Researchers, educators, application developers, and policymakers are evaluating the design, effectiveness, and usability of such human-agent teams and their role in our lives. We envision humans and agents working together in collaborative environments to enrich human lives, support human self-efficacy, increase satisfaction and well-being, improve decision making, and enhance team performance and productivity. With increased connectivity and the transition to knowledge economies, new collaboration environments are emerging. Often, such environments are ad hoc in nature, where humans and autonomous systems have to collaborate without pre-coordination. Such scenarios require the use of new collaboration strategies, particularly those that support rapid adaptation and learning. Therefore, a thorough examination of factors that affect collabo ration efficacy in these types of environments is warranted. A critical part of effective team collaboration is the process and information used to allocate tasks to team members. Effective allocation of tasks to them is particularly challenging in ad hoc teamwork, as prior knowledge about teammate capabilities is either absent or minimal but is critical for the viability and success of such teams. We study task allocation scenarios in which ad hoc and virtual human-agent teams collaborate and adapt over a few interactions to find allocations to improve team effectiveness, measured in terms of team performance and human participant satisfaction. We designed and implemented CHATBoard, a collaborative environment to facilitate task allocation in teams. We developed task allocation protocols in which humans and agents play varying task allocation roles in harnessing the potential of team members with different expertise distribution over task types. We compare team performance and participant satisfaction in experiments with human and agent task allocators in diverse scenarios involving intellective tasks. We vary team composition in terms of the number of team members and if the human believes that the teammate is another human or an autonomous agent. We also investigate team effectiveness for different distributions of agent expertise, including those that are complementary and similar to their human teammates. We develop and evaluate associated research hypotheses through experiments with human participants recruited on the Amazon Mechanical Turk crowdsourcing platform. Our investigation of ad hoc human-agent teams showed that agent task allocators can better leverage the potential of the team, compared to human task allocators, producing higher performance. Humans, however, are less satisfied with the protocol when the agent allocates tasks. To improve human satisfaction without undermining team performance, we investigate alternative allocation mechanisms and approaches. These include allocation protocols providing more control and input for human teammates and approaches that vary agent characteristics and strategies, such as agents providing explanations and guidance. This dissertation addresses a novel area in the literature: how to allocate tasks without pre-coordination so that existing expertise in team members is maximally realized while ensuring the satisfaction of human team members. Our findings herein provide novel and significant insights for researchers and designers developing ad hoc and general human-agent teams.5 0Item Restricted Optimizing Task Allocation for Edge Compute Micro-Clusters(2023-07-24) Alhaizaey, Yousef; Singer, JeremyThere are over 30 billion devices at the network edge. This is largely driven by the unprecedented growth of the Internet-of-Things (IoT) and 5G technologies. These devices are being used in various applications and technologies, including but not limited to smart city systems, innovative agriculture management systems, and intelligent home systems. Deployment issues like networking and privacy problems dictate that computing should occur close to the data source at or near the network edge. Edge and fog computing are recent decentralised computing paradigms proposed to augment cloud services by extending computing and storage capabilities to the network's edge to enable executing computational workloads locally. The benefits can help to solve issues such as reducing the strain on networking backhaul, improving network latency and enhancing application responsiveness. Many edge and fog computing deployment solutions and infrastructures are being employed to deliver cloud resources and services at the edge of the network — for example, cloudless and mobile edge computing. This thesis focuses on edge micro-cluster platforms for edge computing. Edge computing micro-cluster platforms are small, compact, and decentralised groups of interconnected computing resources located close to the edge of a network. These micro-clusters can typically comprise a variety of heterogeneous but resource-constrained computing resources, such as small compute nodes like Single Board Computers (SBCs), storage devices, and networking equipment deployed in local area networks such as smart home management. The goal of edge computing micro-clusters is to bring computation and data storage closer to IoT devices and sensors to improve the performance and reliability of distributed systems. Resource management and workload allocation represent a substantial challenge for such resource-limited and heterogeneous micro-clusters because of diversity in system architecture. Therefore, task allocation and workload management are complex problems in such micro-clusters. This thesis investigates the feasibility of edge micro-cluster platforms for edge computation. Specifically, the thesis examines the performance of micro-clusters to execute IoT applications. Furthermore, the thesis involves the evaluation of various optimisation techniques for task allocation and workload management in edge compute micro-cluster platforms. This thesis involves the application of various optimisation techniques, including simple heuristics-based optimisations, mathematical-based optimisation and metaheuristic optimisation techniques, to optimise task allocation problems in reconfigurable edge computing micro-clusters. The implementation and performance evaluations take place in a configured edge realistic environment using a constructed micro-cluster system comprised of a group of heterogeneous computing nodes and utilising a set of edge-relevant applications benchmark. The research overall characterises and demonstrates a feasible use case for micro-cluster platforms for edge computing environments and provides insight into the performance of various task allocation optimisation techniques for such micro-cluster systems.12 0