The efficiency, security and resiliency are very important factors for the operation of a distribution power system. Taking into account customer demand and energy resource constraints, electric utilities not only need to provide reliable services but also need to operate a power grid as efficiently as possible. The objective of this dissertation is to design, develop and deploy the Multi-Agent Systems (MAS) - together with control algorithms - that enable demand response (DR) implementation at the customer level, focusing on both residential and commercial customers. For residential applications, the main objective is to propose an approach for a smart distribution transformer management. The DR objective at a distribution transformer is to ensure that the instantaneous power demand at a distribution transformer is kept below a certain demand limit while impacts of demand restrike are minimized. The DR objectives at residential homes are to secure critical loads, mitigate occupant comfort violation, and minimize appliance run-time after a DR event. For commercial applications, the goal is to propose a MAS architecture and platform that help facilitate the implementation of a Critical Peak Pricing (CPP) program. Main objectives of the proposed DR algorithm are to minimize power demand and energy consumption during a period that a CPP event is called out, to minimize occupant comfort violation, to minimize impacts of demand restrike after a CPP event, as well as to control the device operation to avoid restrikes. Overall, this study provides an insight into the design and implementation of MAS, together with associated control algorithms for DR implementation in smart buildings. The proposed approaches can serve as alternative solutions to the current practices of electric utilities to engage end-use customers to participate in DR programs where occupancy level, tenant comfort condition and preference, as well as controllable devices and sensors are taken into account in both simulated and real-world environments. Research findings show that the proposed DR algorithms can perform effectively and efficiently during a DR event in residential homes and during the CPP event in commercial buildings. / Ph. D.

Unfavorable environmental and (or) human displacement may engender the need for Search and Rescue (SAR). Challenges such as inaccessibility, large search areas, and heavy reliance on available responder count, limited equipment and training makes SAR a challenging problem. Additionally, SAR operations also pose significant risk to involved responders. This opens a remarkable opportunity for robotic systems to assist and augment human understanding of the harsh environments. A large body of work exists on the introduction of ground and aerial robots in visual and temporal inspection of search areas with varying levels of autonomy. Unfortunately, limited autonomy is the norm in such systems, due to the limitations presented by on-board UAV resources and networking capabilities. In this work we propose a new multi-agent approach to SAR and introduce a wearable compute cluster in the form factor of a backpack. The backpack allows offloading compute intensive tasks such as Lost Person Behavior Modelling, Path Planning and Deep Neural Network based computer vision applications away from the UAVs and offers significantly high performance computers to execute them. The backpack also provides for a strong networking backbone and task orchestrators which allow for enhanced coordination and resource sharing among all the agents in the system. On the basis of our benchmarking experiments, we observe that the backpack can significantly boost capabilities and success in modern SAR responses. / Master of Science / Unfavorable environmental and (or) human displacement may engender the need for Search and Rescue (SAR). Challenges such as inaccessibility, large search areas, and heavy reliance on available responder count, limited equipment and training makes SAR a challenging problem. Additionally, SAR operations also pose significant risk to involved responders. This opens a remarkable opportunity for robotic systems to assist and augment human understanding of the harsh environments. A large body of work exists on the introduction of ground and aerial robots in visual and temporal inspection of search areas with varying levels of autonomy. Unfortunately, limited autonomy is the norm in such systems, due to the limitations presented by on-board UAV resources and networking capabilities. In this work we propose a new multi-agent approach to SAR and introduce a wearable compute cluster in the form factor of a backpack. The backpack allows offloading compute intensive tasks such as Lost Person Behavior Modelling, Path Planning and Deep Neural Network based computer vision applications away from the UAVs and offers significantly high performance computers to execute them. The backpack also provides for a strong networking backbone and task orchestrators which allow for enhanced coordination and resource sharing among all the agents in the system. On the basis of our benchmarking experiments, we observe that the backpack can significantly boost capabilities and success in modern SAR responses.

<p dir="ltr">Increased frequency and intensity of disasters necessitate the dynamic post-disaster recovery process. Developing human mobility patterns, household return decision-making models, and agent-based simulations in disaster management has opened a new door towards more intricate and enduring recovery frameworks. Despite these opportunities, the importance of a unified framework is underestimated to identify the underlying mechanisms hindering the post-disaster recovery process. My research has been geared towards forging advancements in civil and disaster management, focusing on two main areas: (1) modeling the post-disaster recovery process and (2) identifying critical transitions within the recovery process.</p><p dir="ltr">My dissertation explores the collective and individual dynamics of post-disaster recovery across different spatial and temporal scales. I have identified the best recovery strategies for various contexts by constructing data-driven socio-physical multi-agent systems. Employing various advanced computational methodologies, including machine learning, system dynamics, causal discovery, econometrics, and network analysis, has been instrumental. I start with aggregated level analysis for post-disaster recovery. Initially, I examined the system dynamics model for the post-discovery recovery process in socio-physical systems, using normalized visit density of points of interest and power outage information. Through counterfactual analyses of budget allocation strategies, I discovered their significant impact on recovery trajectories, noting that specific budget allocations substantially enhance recovery patterns. I also revealed the urban-rural dissimilarity by the data-driven causal discovery approach. I utilized county-level normalized visit density of points of interest and nighttime light data to identify the relationship between counties. I found that urban and rural areas have similar but different recovery patterns across different types of points of interest.</p><p dir="ltr">Moving from aggregated to disaggregated level analysis on post-disaster recovery, I investigated household-level decision-making regarding disaster-induced evacuation and return behaviors. The model yielded insights into the varying influences of certain variables across urban and rural contexts. Subsequently, I developed a unified framework integrating aggregated and disaggregated level analyses through multilayer multi-agent systems to model significant shifts in the post-disaster recovery process. I evaluated various scenarios to pinpoint conditions for boosting recovery and assessing the effects of different intervention strategies on these transitions. Lastly, a comparison between mathematical models and graph convolutional networks was conducted to better understand the conditions leading to critical transitions in the recovery process. The insights and methodologies presented in this dissertation contribute to the broader understanding of the disaster recovery process in complex urban systems, advocating for a shift towards a unified framework over individual models. By harnessing big data and complex systems modeling, I can achieve a detailed quantitative analysis of the disaster recovery process, including critical transition conditions of the post-disaster recovery. This approach facilitates the evaluation of such recovery policies through inter-regional comparisons and the testing of various policy interventions in counterfactual scenarios.</p>

A cyber-physical control system (CPS) typically consists of a set of physical subsystems, their remote terminal units, a central control center (if applicable), and local communication networks that interconnect all the components to achieve a common goal. Applications include energy systems, autonomous vehicles, and collaborative robots. Ensuring stability, performance, and resilience in CPS requires thorough analysis and control design, utilizing robust algorithms to handle delays, communication failures, and potential cyber-attacks. Time delays are a challenge in CPS, particularly in teleoperation systems, where human operators remotely control robotic systems. These delays cause chattering, oscillations, and instability, making it difficult to achieve smooth and stable remote robot control. Applications like remote surgery, space exploration, and hazardous environment operations are highly susceptible to these disruptions. To address this issue, a novel passivity-shortage framework is proposed, that enables systems to maintain stability and transparency despite time-varying communication delays and environmental disturbances. CPS are prone to attacks, particularly Denial-of-Service (DoS) attacks, which disrupt the normal functioning of a network by overwhelming it with excessive internet traffic, rendering the communication channels unavailable to legitimate users. These attacks threaten the stability and functionality of CPS. To enhance resilience in multi-agent systems, novel distributed algorithms are proposed. These graph theory-based algorithms mitigate network vulnerabilities by incorporating strategically placed additional communication channels, thereby increasing tolerance to attacks in large, dynamic networks. The effectiveness of these proposed approaches is validated through simulations, experiments, and numerical examples. The passivity-shortage teleoperation strategies are tested using Phantom Omni devices and they show reduced chattering and better steady-state error convergence. A case study demonstrates how the proposed distributed algorithms effectively achieve consensus, even when some agents are disconnected from the network due to DoS attacks.

Optimizing connected automated vehicle movements through roadway intersections is a challenging problem. Traditional traffic control strategies, such as traffic signals are not optimal, especially for heavy traffic. Alternatively, centralized automated vehicle control strategies are costly and not scalable given that the ability of a central controller to track and schedule the movement of hundreds of vehicles in real-time is highly questionable. In this research, a series of fully distributed heuristic algorithms are proposed where vehicles in the vicinity of an intersection continuously cooperate with each other to develop a schedule that allows them to safely proceed through the intersection while incurring minimum delays. An algorithm is proposed for the case of an isolated intersection then a number of algorithms are proposed for a network of intersections where neighboring intersections communicate directly or indirectly to help the distributed control at each intersection makes a better estimation of traffic in the whole network. An algorithm based on the Godunov scheme outperformed optimized signalized control. The simulated experiments show significant reductions in the average delay. The base algorithm is successfully added to the INTEGRATION micro-simulation model and the results demonstrate improvements in delay, fuel consumption, and emissions when compared to roundabout, signalized, and stop sign controlled intersections. The study also shows the capability of the proposed technique to favor emergency vehicles, producing significant increases in mobility with minimum delays to the other vehicles in the network. / Ph. D. / Intelligent self-driving cars are getting much closer to reality than fiction. Technological advances make it feasible to produce such vehicles at low affordable cost. This type of vehicles is also promising to significantly reduce car accidents saving people lives and health. Moreover, the congested roads in cities and metropolitan areas especially at rush hours can benefit from this technology to avoid or at least to reduce the delays experienced by car passengers during their trips. One major challenge facing the operation of an intelligent self-driving car is how to pass an intersection as fast as possible without any collision with cars approaching from other directions of the intersection. The use of current traffic lights or stop signs is not the best choice to make the best use of the capabilities of future cars. In this dissertation, the aim is to study and propose ways to make sure the future intersections are ready for such self-driving intelligent cars. Assuming that an intersection has no type of traditional controls such as traffic lights or stop signs, this research effort shows how vehicles can pass safely with minimum waiting. The proposed techniques focus on providing lowcost solutions that do not require installation of expensive devices at intersections that makes it difficult to be approved by authorities. The proposed techniques can be applied to intersections of various sizes. The algorithms in this dissertation carefully design a way for vehicles in a network of intersections to communicate and cooperate while passing an intersection. The algorithms are extensively compared to the case of using traffic lights, stop signs, and roundabouts. Results show significant improvement in delay reduction and fuel consumption when the proposed techniques are used.

The security and resiliency of electric power supply to serve critical facilities are of high importance in today's world. Instead of building large electric power grids and high capacity transmission lines, an intelligent microgrid (or smart grid) can be considered as a promising power supply alternative. In recent years, multi-agent systems have been proposed to provide intelligent energy control and management systems in microgrids. Multi-agent systems offer their inherent benefits of flexibility, extensibility, autonomy, reduced maintenance and more. The implementation of a control network based on multi-agent systems that is capable of making intelligent decisions on behalf of the user has become an area of intense research. Many previous works have proposed multi-agent system architectures that deal with buying and selling of energy within a microgrid and algorithms for auction systems. The others proposed frameworks for multi-agent systems that could be further developed for real life control of microgrid systems. However, most proposed methods ignore the process of sharing energy resources among multiple distinct sets of prioritized loads. It is important to study a scenario that emphasizes on supporting critical loads during outages based on the user's preferences and limited capacity. The situation becomes further appealing when an excess DER capacity after supplying critical loads is allocated to support non-critical loads that belong to multiple users. The previous works also ignore the study of dynamic interactions between the agents and the physical systems. It is important to study the interaction and time delay when an agent issues a control signal to control a physical device in a microgrid and when the command is executed. Agents must be able to respond to the information sensed from the external environment quickly enough to manage the microgrid in a timely fashion. The ability of agents to disconnect the microgrid during emergencies should also be studied. These issues are identified as knowledge gaps that are of focus in this thesis. The objective of this research is to design, develop and implement a multi-agent system that enables real-time management of a microgrid. These include securing critical loads and supporting non-critical loads belonging to various owners with the distributed energy resource that has limited capacity during outages. The system under study consists of physical (microgrid) and cyber elements (multi-agent system). The cyber part or the multi-agent system is of primary focus of this work. The microgrid simulation has been implemented in Matlab/Simulink. It is a simplified distribution circuit that consists of one distributed energy resources (DER), loads and the main grid power supply. For the multi-agent system implementation, various open source agent building toolkits are compared to identify the most suitable agent toolkit for implementation in the proposed multi-agent system. The agent architecture is then designed by dividing overall goal of the system into several smaller tasks and assigning them to each agent. The implementation of multi-agent system was completed by identifying Roles (Role Modeling) and Responsibilities (Social and Domain Responsibilities) of agents in the system, and modeling the Knowledge (Facts), rules and ontology for the agents. Finally, both microgrid simulation and multi-agent system are connected together via TCP/IP using external java programming and a third party TCP server in the Matlab/Simulink environment. In summary, the multi-agent system is designed, developed and implemented in several simulation test cases. It is expected that this work will provide an insight into the design and development of a multi-agent system, as well as serving as a basis for practical implementation of an agent-based technology in a microgrid environment. Furthermore, the work also contributes to new design schemes to increase multi-agent system's intelligence. In particular, these include control algorithms for intelligently managing the limited supply from a DER during emergencies to secure critical loads, and at the same time supporting non-critical loads when the users need the most. / Master of Science

One of the major planning problems faced by medium and large manufacturing enterprises is the distribution of production over various (production) facilities. The need for cross-facility capacity management is most evident in the high-tech industries having capital-intensive equipment and short technology life cycle. There have been solutions proposed in the literature that are based on the lagragian decomposition method which separate the overall multiple product problem into a number of single product problems. We believe that multi-agent systems, given their distributed problem solving approach can be used to solve this problem, in its entirety, more effectively. According to other researchers who have worked in this field, auction theoretic mechanisms are a good way to solve complex production planning problems. This research study develops a multi-agent system and negotiation protocol based on combinatorial auction framework to solve the given multi-facility planning problem. The output of this research is a software library, which can be used as a multi-agent system model of the multi-product, multi-facility capacity allocation problem. The negotiation protocol for the agents is based on an iterative combinatorial auction framework which can be used for making allocation decisions in this environment in real-time. A simulator based on this library is created to validate the multi-agent model as well as the auction theoretic framework for different scenarios in the problem domain. The planning software library is created using open source standards so that it can be seamlessly integrated with scheduling library being developed as a part of the Advanced Planning and Scheduling (APS) system project or any other software suite which might require this functionality. The research contribution of this study is in terms of a new multi-agent architecture for an Advanced Planning and Control (APS) system as well as a novel iterative combinatorial auction mechanism which can be used as an agent negotiation protocol within this architecture. The theoretical concepts introduced by this research are implemented in the MultiPlanner production planning tool which can be used for generating master production plans for manufacturing enterprises. The validation process carried out on both the iterative combinatorial framework and the agent-based production planning methodology demonstrate that the proposed solution strategies can be used for integrated decision making in the multi-product, multi-facility production planning domain. Also, the software tool developed as part of this research is a robust, platform independent tool which can be used by manufacturing enterprises to make relevant production planning decisions. / Master of Science

A tecnologia de agentes tem sido reconhecida como um paradigma promissor em sistemas educacionais da nova geração. Entretanto, o esforço e inflexibilidade de algumas metodologias próprias para agentesacarretam num alto custo, tempo e adaptação de escopo. Este trabalho visaavaliar alternativas de desenvolvimento de um jogo educacional médico orientado a agentes, através da aplicação de um estudo de caso, com o intuito de verificar se metodologias próprias para implementação de sistemas multiagentes trazem benefícios no resultado final da implementação do jogo, e também se os resultados alcançados na comparação de processos de desenvolvimento de cunho tradicional e ágil fazem diferença no resultado final. Desta forma, este trabalho compara três metodologias baseadas nos conceitos da Engenharia de Software através de um estudo de caso, sendo elas: O-MaSE que é uma metodologiatradicional de desenvolvimento de sistemas multiagentes e utiliza um processo de desenvolvimento tradicional; AgilePASSI que é baseada no processo de desenvolvimento ágil e específica para sistemas multiagentes; e, por último, Scrum que é uma metodologia ágil, não sendo específica para implementação de sistemas multiagentes / The agent technology has been recognized as a promising paradigm in educational systems of the new generation. However, the effort and inflexibility of some specific methodologies entail a high cost, time and adaptation scope. This work aims to validate options for developing an educational medical game oriented agents by applying an experiment in order to verify that methodologies specific to implement multi-agent systems provide benefits in the result of the implementation of the game, and also the results achieved by comparison of traditional and agile development processes makes a difference in the outcome. Thus, this paper compares three approaches based on the concepts of software engineering through an experiment, as follows: O-MaSE is a traditional methodology for the development of multi-agent systems and uses a traditional development process; AgilePASSI which is based on agile and specific development for multi-agent systems; and finally, Scrum that is an agile methodology, not specific to implementation of multi-agent systems.

A tecnologia de agentes tem sido reconhecida como um paradigma promissor em sistemas educacionais da nova geração. Entretanto, o esforço e inflexibilidade de algumas metodologias próprias para agentesacarretam num alto custo, tempo e adaptação de escopo. Este trabalho visaavaliar alternativas de desenvolvimento de um jogo educacional médico orientado a agentes, através da aplicação de um estudo de caso, com o intuito de verificar se metodologias próprias para implementação de sistemas multiagentes trazem benefícios no resultado final da implementação do jogo, e também se os resultados alcançados na comparação de processos de desenvolvimento de cunho tradicional e ágil fazem diferença no resultado final. Desta forma, este trabalho compara três metodologias baseadas nos conceitos da Engenharia de Software através de um estudo de caso, sendo elas: O-MaSE que é uma metodologiatradicional de desenvolvimento de sistemas multiagentes e utiliza um processo de desenvolvimento tradicional; AgilePASSI que é baseada no processo de desenvolvimento ágil e específica para sistemas multiagentes; e, por último, Scrum que é uma metodologia ágil, não sendo específica para implementação de sistemas multiagentes / The agent technology has been recognized as a promising paradigm in educational systems of the new generation. However, the effort and inflexibility of some specific methodologies entail a high cost, time and adaptation scope. This work aims to validate options for developing an educational medical game oriented agents by applying an experiment in order to verify that methodologies specific to implement multi-agent systems provide benefits in the result of the implementation of the game, and also the results achieved by comparison of traditional and agile development processes makes a difference in the outcome. Thus, this paper compares three approaches based on the concepts of software engineering through an experiment, as follows: O-MaSE is a traditional methodology for the development of multi-agent systems and uses a traditional development process; AgilePASSI which is based on agile and specific development for multi-agent systems; and finally, Scrum that is an agile methodology, not specific to implementation of multi-agent systems.

Dans cette thèse, nous proposons des modèles logiques pour la représentation des croyances et leur changement dans un cadre multi-agent, en insistant sur l'importance de se fixer un point de vue particulier pour la modélisation. A cet égard, nous distinguons deux approches différentes: l'approche externe, où le modélisateur est quelqu'un d'externe à la situation; l'approche interne, où le modélisateur est l'un des agents. Nous proposons une version interne de la logique épistémique dynamique (avec des modèles d'événements), ce qui nous permet de généraliser facilement la théorie de la révision des croyances d'AGM au cas multi-agent. Ensuite, nous mod´elisons les dynamismes logiques complexes qui soustendent notre interprétation des événements en introduisant des probabilités et des infinitésimaux. Finalement, nous proposons un formalisme alternatif qui n'utilise pas de modèle d'événement mais qui introduit à la place un opérateur d'événement inverse.