Doctor of Philosophy / Department of Computing and Information Sciences / Scott A. DeLoach / Organization-based Multiagent Systems (OMAS) have been viewed as an effective paradigm for addressing the design challenges posed by today’s complex systems. In those systems, the organizational perspective is the main abstraction, which provides a clear separation between agents and systems, allowing a reduction in the complexity of the overall system. To ease the development of OMAS, several methodologies have been proposed. Unfortunately, those methodologies typically require the designer to handle system complexity alone, which tends to lead to ad-hoc designs that are not scalable and are difficult to maintain. Moreover, designing organizations for large multiagent systems is a complex and time-consuming task; design models quickly become unwieldy and thus hard to develop. To cope with theses issues, a framework for organization-based multiagent system designs based on separation of concerns and composition principles is proposed. The framework uses category theory tools to construct a formal composition framework using core models from the Organization-based Multiagent Software Engineering (O-MASE) framework. I propose a formalization of these models that are then used to establish a reusable design approach for OMAS. This approach allows designers to design large multiagent organizations by reusing smaller composable organizations that are developed separately, thus providing them with a scalable approach for designing large and complex OMAS. In this dissertation, the process of formalizing and composing multiagent organizations is discussed. In addition, I propose a service-oriented approach for building autonomous, adaptive multiagent systems. Finally, as a proof of concept, I develop two real world examples from the domain of cooperative robotics and wireless sensor networks.

Fundamental aspects of biologically-inspired regulatory mechanisms are considered in a robotics context, using artificial neural-network control systems . Regulatory mechanisms are used to control expression of genes, adaptation of form and behavior in organisms. Traditional neural network control architectures assume networks of neurons are fixed and are interconnected by wires. However, these architectures tend to be specified by a designer and are faced with several limitations that reduce scalability and tractability for tasks with larger search spaces. Traditional methods used to overcome these limitations with fixed network topologies are to provide more supervision by a designer. More supervision as shown does not guarantee improvement during training particularly when making incorrect assumptions for little known task domains. Biological organisms often do not require such external intervention (more supervision) and have self-organized through adaptation. Artificial neural tissues (ANT) addresses limitations with current neural-network architectures by modeling both wired interactions between neurons and wireless interactions through use of chemical diffusion fields. An evolutionary (Darwinian) selection process is used to ‘breed’ ANT controllers for a task at hand and the framework facilitates emergence of creative solutions since only a system goal function and a generic set of basis behaviours need be defined. Regulatory mechanisms are formed dynamically within ANT through superpositioning of chemical diffusion fields from multiple sources and are used to select neuronal groups. Regulation drives competition and cooperation among neuronal groups and results in areas of specialization forming within the tissue. These regulatory mechanisms are also shown to increase tractability without requiring more supervision using a new statistical theory developed to predict performance characteristics of fixed network topologies. Simulations also confirm the significance of regulatory mechanisms in solving certain tasks found intractable for fixed network topologies. The framework also shows general improvement in training performance against existing fixed-topology neural network controllers for several robotic and control tasks. ANT controllers evolved in a low-fidelity simulation environment have been demonstrated for a number of tasks on hardware using groups of mobile robots and have given insight into self-organizing system. Evidence of sparse activity and use of decentralized, distributed functionality within ANT controller solutions are found consistent with observations from neurobiology.

Multiagent systems offer a design paradigm used to conceptualise and implement systems composed of autonomous agents. Autonomy facilitates proactive independent behaviour yet in practice agents are constrained in order to ensure the system satisfies a desired social objective. Explicit constraints on agent behaviour, in the form of social norms, encourage this desirable system behaviour, yet research has largely focused on norm representation languages and protocols for norm proposal and adoption. The fundamental problem of how to automate the process of norm synthesis has largely been overlooked with norms assumed provided by the designer. Previous work has shown that automating the design of social norms is intractable in the worst case. Existing approaches, relying on state space enumerations, are effective for small systems but impractical for larger ones. Furthermore, they do not produce a set of succinct, general norms but rather a large number of state-specific restrictions. This work presents conflict-rooted synthesis, an automated norm synthesis approach that utilises a planning-based action schemata to overcome these limitations. These action schemata facilitate localised searches around specifications of undesirable states, using representations of sets of system states to avoid a full state enumeration. The proposed technique produces concise, generalised social norms that are applicable in multiple system states while also providing guarantees that agents are still able to achieve their original goals in the constrained system. To improve efficiency a set of theoretically sound, domain-independent optimisations are presented that reduce the state space searched without compromising the quality of the norms synthesised. A comparison with an alternative model checking based technique illustrates the advantages and disadvantages of our approach, while an empirical evaluation highlights the improved efficiency and quality of norms it produces at the cost of a less expressive specification of undesirable states. We empirically investigate the effectiveness of each of the proposed optimisations using a set of benchmark domains, quantifying how successful each of them is at reducing search complexity in practice. The results show that, with all optimisations enabled, conflict-rooted synthesis produces more generally applicable and succinct norms and consumes fewer system resources. Additionally, we show that this approach synthesises norms in systems where the competing approach is intractable. We provide a discussion of our approach, highlighting the impact our abstract search approach has on the fields of multiagent systems and automated planning, and discuss the limitations and assumptions we have made. We conclude with a presentation of future work.

This thesis provides an implementation of consensus of multi-agent networked systems. Consensus problem is an important issue of distributed computing and has various algorithms and applications in the field of electronical and computer science. The consensus requests all nodes of a network reach an agreement over a certain measurement. An algorithm of convergent consensus problem is implemented through a small network of Bluetooth communication in the thesis. The connections of the Bluetooth devices are wireless, and the device nodes of the network are driven by C++ software and Winsock API. The simulation results show that the implementation completes all the requirements of the distributed consensus algorithm.

Os múltiplos trabalhos de sistemas multiagentes musicais realizados nos últimos anos demonstram o interesse crescente na pesquisa de sistemas de composição e de performance musical que utilizem a tecnologia de agentes computacionais, sendo que apresentam um interesse maior por aqueles sistemas que integram técnicas de composição algorítmica, componentes de vida artificial e interatividade. Observamos também que a maioria dos trabalhos existentes apresentam muitas limitações em termos de escopo e flexibilidade, normalmente apresentando codificação musical simbólica e a resolução de um único problema, sendo que a motivação é mais técnica do que musical. Nesse contexto, surgem arcabouços voltados à criação de sistemas multiagentes musicais, como o Ensemble e o Interactive Swarm Orchestra, oferecendo flexibilidade para a modelagem e implementação de sistemas desse tipo, diversificando tanto os tipos de aplicação, tendo um propósito composicional ou performático, como os tipos de codificação musical que podem ser utilizados. Partimos da aparição dessas ferramentas para estudar o agente musical a partir de uma perspectiva interna, focando nos seus raciocínios, que são processos que definem o comportamento do agente no ambiente virtual do sistema e que são fundamentais para determinar e melhorar o seu valor composicional. Os arcabouços estudados se diferenciam por permitir a utilização de áudio como possível formato de codificação musical, o aproveitamento da espacialização sonora e a exploração da interatividade nos aplicativos, seja esta apenas entre agentes computacionais ou entre agentes e usuários humanos. Pretendemos portanto, nessa pesquisa, abordar sistemas com essas características. Através de extensões nos arcabouços e estudos de caso com motivação estética pretendemos dar continuidade a esses projetos e ao mesmo tempo validar e divulgar a sua utilização entre os potenciais usuários das ferramentas, como compositores, músicos interessados em performance e outros entusiastas dos sistemas musicais interativos. / Multiple musical multiagent systems have been developed in the last years proving the increasing interest in composition and musical performance systems that exploit intelligent agents technology. Theres an special focus on systems that integrate algorithmic composition techniques, artificial life and interactivity. We can also observe that most of these existing projects show many flexibility and scope limitations, as they normally use symbolic musical notation and they solve a single issue or scenario, as well as they have a technical motivation rather than a musical one. In that context, some musical multiagent systems frameworks as Ensemble and Interactive Swarm Orchestra emerge, trying to help the modeling and development of this kind of musical systems, diversifying the applications\' types, as they can be composition problems or musical performances, and allowing the inclusion of other kind of musical content communication. Through these new tools we study the musical agent from an internal perspective, focusing on its reasoning components, processes that define the behavior of an agent on its system\'s virtual environment and that are essential to determine and improve its compositional value. The studied frameworks show unique features as they support audio as a possible musical notation format; they exploit sound spatialization and they work with interactivity in their applications, including agent-to-agent or human-to-agent interaction. We will explore this type of systems on this research. Through framework extensions and aesthetics-oriented study cases we pretend to continue these projects and validate them at same time. We also will contact potential users for these tools, as composers and musicians interested in performances or other musical interactive systems enthusiasts.

Nesta tese, propõe-se integrar, de modo sistemático, modelos organizacionais para prover interoperabilidade organizacional em sistemas multiagentes abertos. A interoperabilidade organizacional consiste em permitir que, em uma mesma organização de agentes, coexistam agentes desenvolvidos a partir de modelos organizacionais distintos. Para realizar tal intuito, aplica-se uma abordagem baseada em técnicas da Engenharia Dirigida por Modelos. A abordagem proposta divide-se em três etapas principais: (1) representar a estrutura conceitual de modelos organizacionais através de metamodelos; (2) identificar correspondências entre as estruturas conceituais dos modelos organizacionais utilizando como heurística a idéia de dimensões e padrões de modelagem organizacional ; e (3) combinar os metamodelos dos modelos organizacionais seguindo um algoritmo de junção de modelos baseado em grafos e articulações de grafos. Utilizando a abordagem proposta, quatro modelos organizacionais existentes são integrados: os modelos AGR, STEAM, MOISE+ e ISLANDER. Para ilustrar o uso da integração de metamodelos no problema de interoperabilidade organizacional, especifica-se e implementa-se uma infraestrutura de interoperabilidade organizacional chamada MAORI (Model-driven Architecture for Organizational Interoperability). A infraestrutura MAORI é estruturada em duas camadas: (1) a camada M2M, na qual a integraçãoo de modelos realizada é utilizada para traduzir especificações e estados organizacionais, e (2) a camada ORI, na qual infraestruturas organizacionais são interconectadas. Para testar e validar toda a proposta, a infraestrutura MAORI foi implementada para as infraestruturas organizacionais S-MOISE+ e MADKIT, e uma aplicaçào denominada WritePaper foi desenvolvida. A partir das implementações, alguns experimentos foram realizados. Os resultados obtidos demonstram que a integração de modelos organizacionais proposta, realizada na infraestrutura de interoperabilidade MAORI, efetivamente interconecta infraestruturas organizacionais distintas e permite que, em uma mesma organização de agentes, coexistam agentes desenvolvidos a partir de modelos organizacionais distintos. / This thesis proposes to integrate systematically organizational models for providing organizational interoperability in open multiagent systems. Organizational interoperability consists in enabling the participation, in the same agent organization, of agents developed with different organizational models. For this purpose, it is applied an approach based on techniques from Model-driven Engineering. The approach is divided in three main steps: (1) to represent the conceptual structure of organizational models by means of metamodels; (2) to identify correspondences among the conceptual structures of the organizational models using as heuristics the idea of organizational modeling dimensions and patterns; (3) to combine the metamodels of the organizational models by following a merge algorithm based on graphs and graph articulations. Using the proposed approach, four existing organizational models are integrated: AGR, STEAM, MOISE+ and ISLANDER. To illustrate the use of the integration, an organizational interoperability infrastructure called MAORI (Model-driven Architecture for Organizational Interoperability) is specified and implemented. The MAORI infrastructure is structured in two layers: (1) the M2M layer, in which the integration of models is used to translate the organizational specifications and states, and (2) the ORI layer, in which the organizational infrastructures are interconnected. To test and validate the approach, MAORI infrastructure was implemented for the organizational infrastructures S-MOISE+ and MADKIT, and an application called Write Paper was developed. Using the implementations, some experiments were done. The results show that the organizational model integration, implemented in the MAORI infrastructure, effectively interconnects distinct organizational infrastructures and enables the participation, in the same agent organization, of agents developed with different organizational models.

Sistemas multiagentes muitas vezes apresentam características que os aproximam de sociedades de agentes e, como as humanas, possuem normas e organizações com o objetivo de coordenar as interações entre os seus membros. Coalizão é um tipo de estrutura organizacional temporária, montada com objetivos específicos. A teoria dos jogos estuda formalmente o fenômeno coalizional, se detendo em demonstrações de propriedades e características dessa estrutura. A área de sistemas multiagentes, por outro lado, tem mostrado significativo interesse nas estruturas coalizionais como forma de organizar a cooperação entre os agentes, dedicando-se ao desenvolvimento de algoritmos para formação de coalizões. Esse trabalho apresenta um algoritmo de formação de coalizões para compartilhamento de recursos, denominado heurística ganha-ganha. Definimos um modelo que utiliza a abstração de recursos para representar tanto a posse de habilidades e objetos, quanto para representar os objetivos dos agentes. Um jogo de votação k-ponderado é utilizado para implementar o processo decisório de quais coalizões são válidas e o algoritmo testa iterativamente cada vizinhança de um agente na busca de associações vantajosas. Demonstramos que o algoritmo incrementa monotonicamente o bem-estar da sociedade e converge para uma estrutura coalizional. Também mostramos empiricamente que a heurística é eficiente para compartilhamento de recursos em situações de abundância de recursos, montando coalizões em poucas iterações e com uma quantidade grande de agentes. / Multiagent systems frequently show characteristics that come closer to agent societies and, like the humans ones, have norms and organizations in order to coordinate the interactions of its members. Coalition is a type of temporary organizational structure, assembled with specific goals. Game theory formally studies the coalitional phenomenon focusing in the demonstrations of properties and characteristics of this structure. The area of multiagent systems, on the other hand, has devoted significant interest in coalition structures as a way to organize cooperation between its members, and has been dedicated to the development of algorithms for coalition formation. This dissertation presents an algorithm to coalition formation named win-win heuristic. We define a model which uses the abstraction of resources to represent either, the possession of abilities and objects, or to represent the agents target. A k-weight voting game is used to implement the decision process of what coalitions are worth and an iteratively algorithm tests each agent neighborhood in the pursue of better associations. We demonstrate that the algorithm monotonically increases the society welfare and converges to a coalitional structure. We also show empirically that the heuristic is efficient for resource sharing in situations of availability of resources, building coalitions with few iterations and a large amount of agents.

Esta tese propõe um modelo de adaptação organizacional em Sistemas Multiagentes (SMA) composto por um modelo de organização e um processo de reorganização. O modelo organizacional, chamado de MOISE+, considera três dimensões da organização de um SMA: a estrutura (grupos, papéis e ligações), o funcionamento (planos globais, metas e missões) e as obrigações e permissões dos agentes. A principal característica do modelo MOISE+ é facilitar a mudança organizacional em uma das dimensões sem comprometer as outras. É possível, por exemplo, alterar a estrutura da organização sem alterar o seu funcionamento e vice-versa. O processo de reorganização permite que os próprios agentes realizem a mudança através de quatro etapas: monitoração da organização corrente, projetos de planos de mudança, seleção dos planos e implementação do plano selecionado. A principal característica deste processo é a existência explícita da organização responsável pela reorganização (descrita na notação do modelo MOISE+) permitindo a abertura do processo. Estando a representação deste processo disponível aos agentes, estes podem participar da reorganização, bastando que compreendam a notação do modelo. Este processo foi implementado e avaliado em dois estudos de caso, empresas virtuais e futebol de robôs, nos quais as etapas de seleção e implementação foram enfatizadas, já que existem poucos trabalhos que abordam estas duas etapas do processo de reorganização. / This thesis proposes a reorganization model for Multiagent Systems (MAS). This reorganization model is composed by both an organizational model and a reorganization process. The organization model, called MOISE+, is based on three dimensions: the organizational structure (composed by roles, groups, links, etc.), the organizational functioning (composed by global plans, missions, goals, etc.), and the agents\' obligations and permissions. The MOISE+ model main feature is the independence among these dimensions. It is therefore possible to change the organizational structure without any change in the functioning. The reorganization process enables the agents themselves to perform the reorganization along four phases: monitoring (when to reorganize), design (ways of building a new organization), selection (how to choose a new organization), and implementation (how to change the current running organization). The reorganization process is explicitly described in MOISE+ notation and the agents have access to it. This property enables any agent to participate in the reorganization when he understands MOISE+ notation. The reorganization process is thus open since many agents can joint it. This process was implemented and evaluated in two domains: virtual enterprises and robot soccer. The experiments have focused mainly on the selection and implementation phases since the research on those phases is not very common in the literature.

Systems with time delays have a broad range of applications not only in control systems but also in many other disciplines such as mathematical biology, financial economics, etc. The time delays cause more complex behaviours of the systems. It requires more sophisticated analysis due to the infinite dimensional structure of the space spaces. In this thesis we investigate stability properties associated with output functions of delay systems. Our primary target is the equivalent Lyapunov characterization of input-tooutput stability (ios). A main approach used in this work is the Lyapuno Krasovskii functional method. The Lyapunov characterization of the so called output-Lagrange stability is technically the backbone of this work, as it induces a Lyapunov description for all the other output stability properties, in particular for ios. In the study, we consider two types of output functions. The first type is defined in between Banach spaces, whereas the second type is defined between Euclidean spaces. The Lyapunov characterization for the first type of output maps provides equivalence between the stability properties and the existence of the Lyapunov-Krasovskii functionals. On the other hand, as a special case of the first type, the second type output renders flexible Lyapunov descriptions that are more efficient in applications. In the special case when the output variables represent the complete collection of the state variables, our Lyapunov work lead to Lyapunov characterizations of iss, complementing the current iss theory with some novel results. We also aim at understanding how output stability are affected by the initial data and the external signals. Since the output variables are in general not a full collection of the state variables, the overshoots and decay properties may be affected in different ways by the initial data of either the state variables or just only the output variables. Accordingly, there are different ways of defining notions on output stability, making them mathematically precisely. After presenting the definitions, we explore the connections of these notions. Understanding the relation among the notions is not only mathematically necessary, it also provides guidelines in system control and design. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection

Este trabalho apresenta uma arquitetura para fornecer, de forma automática, o reforço de aprendizagem após a aplicação de uma avaliação objetiva dentro de um ambiente de Educação à Distância. Para o desenvolvimento da arquitetura foram usadas tecnologias das áreas de Ontologia e Agentes. A área de Ontologia contribuiu com a construção de materiais de aprendizagem modelados ontologicamente e a construção de ontologias de domínio das disciplinas de Estruturas de Dados I e Análise e Desenvolvimento de Algoritmos I, ministradas nos cursos de Bacharelado em Ciência da Computação e Bacharelado em Sistemas de Informação oferecidos pelo Departamento de Computação da Faculdade de Ciências da UNESP – campus de Bauru - SP. A área de Agentes traz, ao ambiente, agentes de busca capazes de fazer indicações de reforço do conteúdo. A arquitetura foi implementada usando o ambiente de Educação à Distância VirtualCurso, desenvolvido originalmente pelo Laboratório de Tecnologia de Informação Aplicada (LTIA) da UNESP – campus de Bauru - SP. A implementação recebeu o nome de VCurso+. Realizou-se alguns testes com o sistema para verificar se o reforço obteve o efeito esperado na aprendizagem do aluno. / This work presents an architecture to support automatically the apprentice’s reinforcing on environment of Distance Education. In order to develop this architecture technologies from the areas of Ontology & Agents have been used. The Ontology domain has contributed with the building of apprentice’s materials and the construction of domain ontologies for the disciplines of Data Structures I and Analysis & Development of Algorithms I, taught on bachelor's degree of Computer Science and bachelor's degree in Information Systems courses offered at the Computer Science Department of UNESP campus of Bauru SP. The Agents domain has inspered the creation of search agents capable of making indications of content reinforcing. The proposed architecture was implemented in the VirtualCurso enviroment, developped originally at Laboratory of Technology of Information Applied (LTIA) from UNESP campus of Bauru SP and it was called VCurso +. Some tests have been made with the system to verify whether the reinforcing learning proposed by architecture has had the expected effect on the apprentice’s learning.