Theses and Dissertations from UMD
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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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Item Assured Autonomy in Multiagent Systems with Safe Learning(2022) Fiaz, Usman Amin; Baras, John S; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Autonomous multiagent systems is an area that is currently receiving increasing attention in the communities of robotics, control systems, and machine learning (ML) and artificial intelligence (AI). It is evident today, how autonomous robots and vehicles can help us shape our future. Teams of robots are being used to help identify and rescue survivors in case of a natural disaster for instance. There we understand that we are talking minutes and seconds that can decide whether you can save a person's life or not. This example portrays not only the value of safety but also the significance of time, in planning complex missions with autonomous agents. This thesis aims to develop a generic, composable framework for a multiagent system (of robots or vehicles), which can safely carry out time-critical missions in a distributed and autonomous fashion. The goal is to provide formal guarantees on both safety and finite-time mission completion in real time, thus, to answer the question: “how trustworthy is the autonomy of a multi-robot system in a complex mission?” We refer to this notion of autonomy in multiagent systems as assured or trusted autonomy, which is currently a very sought-after area of research, thanks to its enormous applications in autonomous driving for instance. There are two interconnected components of this thesis. In the first part, using tools from control theory (optimal control), formal methods (temporal logic and hybrid automata), and optimization (mixed-integer programming), we propose multiple variants of (almost) realtime planning algorithms, which provide formal guarantees on safety and finite-time mission completion for a multiagent system in a complex mission. Our proposed framework is hybrid, distributed, and inherently composable, as it uses a divide-and-conquer approach for planning a complex mission, by breaking it down into several sub-tasks. This approach enables us to implement the resulting algorithms on robots with limited computational power, while still achieving close to realtime performance. We validate the efficacy of our methods on multiple use cases such as autonomous search and rescue with a team of unmanned aerial vehicles (UAVs) and ground robots, autonomous aerial grasping and navigation, UAV-based surveillance, and UAV-based inspection tasks in industrial environments. In the second part, our goal is to translate and adapt these developed algorithms to safely learn actions and policies for robots in dynamic environments, so that they can accomplish their mission even in the presence of uncertainty. To accomplish this goal, we introduce the ideas of self-monitoring and self-correction for agents using hybrid automata theory and model predictive control (MPC). Self-monitoring and self-correction refer to the problems in autonomy where the autonomous agents monitor their performance, detect deviations from normal or expected behavior, and learn to adjust both the description of their mission/task and their performance online, to maintain the expected behavior and performance. In this setting, we propose a formal and composable notion of safety and adaptation for autonomous multiagent systems, which we refer to as safe learning. We revisit one of the earlier use cases to demonstrate the capabilities of our approach for a team of autonomous UAVs in a surveillance and search and rescue mission scenario. Despite portraying results mainly for UAVs in this thesis, we argue that the proposed planning framework is transferable to any team of autonomous agents, under some realistic assumptions. We hope that this research will serve several modern applications of public interest, such as autopilots and flight controllers, autonomous driving systems (ADS), autonomous UAV missions such as aerial grasping and package delivery with drones etc., by improving upon the existing safety of their autonomous operation.Item Agent Modeling in Stochastic Repeated Games(2014) Cheng, Kan Leung; Nau, Dana S.; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)There are many situations in which two or more agents (e.g., human or computer decision makers) interact with each other repeatedly in settings that can be modeled as repeated games. In such situations, there is evidence that agents sometimes deviate greatly from what conventional game theory would predict. There are several reasons why this might happen, one of which is the focus of this dissertation: sometimes an agent's preferences may involve not only its own payoff (as specified in the payoff matrix), but also the payoffs of the other agent(s). In such situations, it is important to be able to understand what an agent's preferences really are, and how those preferences may affect the agent's behavior. This dissertation studies how the notion of Social Value Orientation (SVO), a construct in social psychology to model and measure a person's social preference, can be used to improve our understanding of the behavior of computer agents. Most of the work involves the life game, a repeated game in which the stage game is chosen stochastically at each iteration. The work includes the following results: * Using a combination of the SVO theory and evolutionary game theory, we studied how an agent's SVO affects its behavior in Iterated Prisoner's Dilemma (IPD). Our analysis provides a way to predict outcomes of agents playing IPD given their SVO values. * In the life game, we developed a way to build a model of agent's SVO based on observations of its behavior. Experimental results demonstrate that the modeling technique works well. * We performed experiments showing that the measured social preferences of computer agents have significant correlation with that of their human designers. The experimental results also show that knowing the SVO of an agent's human designer can be used to improve the performance of other agents that interact with the given agent. * A limitation of the SVO model is that it only looks at agents' preferences in one-shot games. This is inadequate for repeated games, in which an agent's actions may depend on both its SVO and whatever predictions it makes of the other agent's behavior. We have developed an extension of the SVO construct called the behavioral signature, a model of how an agent's behavior over time will be affected by both its own SVO and the other agent's SVO. The experimental results show that the behavioral signature is an effective way to generalize SVO to situations where agents interact repeatedly.