<div>Self-organization in a multi-robot system is a spontaneous process where some form of overall order arises from local interactions between robots in an initially disordered system. Cooperative coordination strategies for self-organization promote teamwork to complete a task while increasing the total utility of the system. In this dissertation, we apply prosocial behavioral concepts such as altruism and cooperation in multi-robot systems and investigate their effects on overall system performance on given tasks. We stress the significance of this research in long-term applications involving minimal to no human supervision, where self-sustainability of the multi-robot group is of utmost importance for the success of the mission at hand and system re-usability in the future.</div><div><br></div><div>For part of the research, we take bio-inspiration of cooperation from the huddling behavior of Emperor Penguins in the Antarctic which allows them to share body heat and survive one of the harshest environments on Earth as a group. A cyclic energy sharing concept is proposed for a convoying structured multi-robot group inspired from penguin movement dynamics in a huddle with carefully placed induction coils to facilitate directional energy sharing with neighbors and a position shuffling algorithm, allowing long-term survival of the convoy as a group in the field. Simulation results validate that the cyclic process allows individuals an equal opportunity to be at the center of the group identified as the most energy conserving position, and as a result robot groups were able to travel over 4 times the distance during convoying with the proposed method without any robot failing as opposed to without the shuffling and energy sharing process. </div><div><br></div><div>An artificial potential based Adaptive Inter-agent Spacing (AIS) control law is also proposed for efficient energy distribution in an unstructured multi-robot group aimed at long-term survivability goals in the field. By design, as an altruistic behavior higher energy bearing robots are dispersed throughout the group based on their individual energy levels to counter skewed initial distributions for faster group energy equilibrium attainment. Inspired by multi-huddle merging and splitting behavior of Emperor Penguins, a clustering and sequential merging based systematic energy equilibrium attainment method is also proposed as a supplement to the AIS controller. The proposed system ensures that high energy bearing agents are not over crowded by low energy bearing agents. The AIS controller proposed for the unstructured energy sharing and distribution process yielded 55%, 42%, 23% and 33% performance improvements in equilibrium attainment convergence time for skewed, bi-modal, normal and random initial agent resource level distributions respectively on a 2D plane using the proposed energy distribution method over the control method of no adaptive spacing. Scalability analysis for both energy sharing concepts confirmed their application with consistently improved performances different sized groups of robots. Applicability of the AIS controller as a generalized resource distribution method under certain constraints is also discussed to establish its significance in various multi-robot applications.</div><div><br></div><div>A concept of group based survival from damaging directional external stimuli is also adapted from the Emperor Penguin huddling phenomenon where individuals on the damaging stimuli side continuously relocate to the leeward side of the group following the group boundary using Gaussian Processes Machine Learning based global health-loss rate minima estimations in a distributed manner. The method relies on cooperation from all robots where individuals take turns being sheltered by the group from the damaging external stimuli. The distributed global health loss rate minima estimation allowed the development of two settling conditions. The global health loss rate minima settling method yielded 12.6%, 5.3%, 16.7% and 14.2% improvement in average robot health over the control case of no relocation, while an optimized health loss rate minima settling method further improved on the global health loss rate settling method by 3.9%, 1.9%, 1.7% and 0.6% for robot group sizes 26, 35, 70 and 107 respectively.</div><div><br></div><div>As a direct application case study of collaboration in multi-robot systems, a distributed shape formation strategy is proposed where robots act as beacons to help neighbors settle in a prescribed formation by local signaling. The process is completely distributed in nature and does not require any external control due to the cooperation between robots. Beacon robots looking for a robot to settle as a neighbor and continue the shape formation process, generates a surface gradient throughout the formed shape that allow robots to determine the direction of the structure forming frontier along the dynamically changing structure surface and eventually reach the closest beacon. Simulation experiments validate complex shape formation in 2D and 3D using the proposed method. The importance of group collaboration is emphasized in this case study without which the shape formation process would not be possible, without a centralized control scheme directing individual agents to specific positions in the structure. </div><div> </div><div>As the final application case study, a collaborative multi-agent transportation strategy is proposed for unknown objects with irregular shape and uneven weight distribution. Although, the proposed system is robust to single robot object transportation, the proposed methodology of transport is focused on robots regulating their effort while pushing objects from an identified pushing location hoping other robots support the object moment on the other end of the center of mass to prevent unintended rotation and create an efficient path of the object to the goal. The design of the object transportation strategy takes cooperation cues from human behaviors when coordinating pushing of heavy objects from two ends. Collaboration is achieved when pushing agents can regulate their effort with one another to maintain an efficient path for the object towards the set goal. Numerous experiments of pushing simple shapes such as disks and rectangular boxes and complex arbitrary shapes with increasing number of robots validate the significance and effectiveness of the proposed method. Detailed robustness studies of changing weight of objects during transportation portrayed the importance of cooperation in multi-agent systems in countering unintended drift effects of the object and maintain a steady efficient path to the goal. </div><div><br></div><div>Each case study is presented independent of one another with the Penguin huddling based self-organizations in response to internal and external stimuli focused on fundamental self-organization methods, and the structure formation and object transportation strategies focused on cooperation in specific applications. All case studies are validated by relevant simulation and experiments to establish the effectiveness of altruistic and cooperative behaviors in multi-robot systems.</div>

Game balance has been a challenge for game developers since the time games have become more complex. There have been a handful of proposals for game balancing processes outside the manual labor-intensive play testing methods, which most game developers often are forced to use simply due to the lack of better methods. Simple solutions, like restrictive game play, are limited because of their inability to provide insight on interdependencies among the mechanisms in the game. Complex techniques framed around the potential of AI algorithms are limited by computational budgets or cognition inability to assess human actions. In order to find a middle ground we investigate Game Theory and Mechanism Design concepts. Both have proven to be effective tools to analyse strategic situations among interacting participants, or in this case `players'. We test the feasibility of using these techniques in an Real Time Strategy (RTS) game domain to understand game balance. MicroRTS, a small and simple execution of an RTS game is employed as our model. The results provide promising insight on the effectiveness of the method in detecting imbalances and further inspection to find the cause. An additional benefit out of this technique, besides detecting for game imbalances, the approach can be leveraged to create imbalances. This is useful when the designer or player desires to do so.

<div>Multi-robot systems have demonstrated strong capability in handling environmental operations. In this study, We examine how a team of robots can be utilized in covering and removing spill patches in a dynamic environment by executing three consecutive stages: deployment, coverage, and rendezvous. </div><div> </div><div>For the deployment problem, we aim for robot allocation based on the discreteness of the patches that need to be covered. With the deep neural network (DNN) based spill detector and remote sensing facilities such as drones with vision sensors and satellites, we are able to obtain the spill distribution in the workspace. Then, we formulate the allocation problem in a general optimization form and provide solutions using an integer linear programming (ILP) solver under several realistic constraints. After the allocation process is completed and the robot team is divided according to the number of spills, we deploy robots to their computed optimal goal positions. In the robot deployment part, control laws based on artificial potential field (APF) method are proposed and practiced on robots with a common unicycle model. </div><div> </div><div>For the coverage control problem, we show two strategies that are tailored for a wirelessly networked robot team. We propose strategies for coverage with and without path planning, depending on the availability of global information. Specifically, in terms of coverage with path planning, we partition the workspace from the aerial image into pieces and let each robot take care of one of the pieces. However, path-planning-based coverage relies on GPS signals or other external positioning systems, which are not applicable for indoor or GPS-denied circumstances. Therefore, we propose an asymptotic boundary shrink control that enables a collective coverage operation with the robot team. Such a strategy does not require a planned path, and because of its distributedness, it shows many advantages, including system scalability, dynamic spill adaptability, and collision avoidance. In case of a large-scale patch that poses challenges to robot connectivity maintenance during the operation, we propose a pivot-robot coverage strategy by mean of an a priori geometric tessellation (GT). In the pivot-robot-based coverage strategy, a team of robots is sent to perform complete coverage to every packing area of GT in sequence. Ultimately, the entire spill in the workspace can be covered and removed.</div><div> </div><div>For the rendezvous problem, we investigate the use of graph theory and propose control strategies based on network topology to motivate robots to meet at a designated or the optimal location. The rendezvous control strategies show a strong robustness to some common failures, such as mobility failure and communication failure. To expedite the rendezvous process and enable herding control in a distributed way, we propose a multi-robot multi-point rendezvous control strategy. </div><div> </div><div>To verify the validity of the proposed strategies, we carry out simulations in the Robotarium MATLAB platform, which is an open source swarm robotics experiment testbed, and conduct real experiments involving multiple mobile robots.</div>

<p dir="ltr">The research delves into the intricate challenge of quantifying data protection, a concept that has evolved from ancient ethical codes to the complex landscape of modern cybersecurity. The research underscores the pressing need for a scientific approach to cybersecurity, emphasizing the importance of measurable security properties and a robust theoretical foundation. It highlights the historical evolution of confidentiality, tracing its roots from ancient civilizations to the contemporary digital era, where the proliferation of technology has amplified both the important ortance and complexity of safeguarding sensitive information. The research identifies key challenges in measuring data protection, including the dynamic nature of threats, the gap between theoretical models and real-world implementations, and the difficulty of accurately modeling risks. It also explores societal challenges related to data protection, such as data breaches, surveillance, social media privacy erosion, and the lack of adequate regulations and enforcement.</p><p dir="ltr">The core of the research lies in developing a causal model that examines the interplay of security controls, vulnerabilities,and threats, providing a deeper understanding of the factors influencing data exposure. The model is built upon a comprehensive literature review, synthesizing key findings and establishing a taxonomy of security protections. The research outlines a structured approach to building and utilizing causality models, incorporating essential elements such as identifying key variables, visualizing causal relationships using Directed (A)cyclic Graphs (DAGs), and determining appropriate research methodologies. The model is rigorously validated through various techniques, including assessing model fit, examining confounding factors. The research also explores a general set of experiments for both interventions and counterfactual studies.</p><p dir="ltr">The research concludes by highlighting potential future research directions, particularly emphasizing the need for standardized data protection metrics and the development of adaptive security systems. It underscores the importance of consistent measurements that enable organizations to compare their security performance effectively and adapt to the evolving threat landscape. The development of adaptive security systems, capable of dynamically modifying defense mechanisms in response to new threats, is also identified as a crucial research avenue. The research's contribution lies in providing a systematic approach to studying data protection, from problem identification to model development, validation, and future directions, ultimately aiming to enhance the protection of sensitive information.</p>

<p>This dissertation examines the impact of active shooters upon patrons attending large outdoor events. There has been a spike in shooters targeting densely populated spaces in recent years, to include open-air venues. The 2019 Gilroy Garlic Festival was selected for modeling replication using AnyLogic software to test various experiments designed to reduce casualties in the event of an active shooter situation. Through achievement of validation to produce identical outcomes of the real-world Gilroy Garlic Festival shooting, the researcher established a reliable foundational model for experimental purposes. This active shooter research project identifies the need for rapid response efforts to neutralize the shooter(s) as quickly as possible to minimize casualties. Key findings include the importance of armed officers patrolling event grounds to reduce response time, the need for adequate exits during emergency evacuations, incorporation of modern technology to identify the shooter’s location, and applicability of a 1:548 police to patron ratio.</p>