The thesis work describes the development of a multiagent testing application (MTA) based on an agent approach for solving challenges in regression testing domain, such as: reducing the complexity of testing, optimizing the time consumption, increasing the efficiency and implementing the automation of this approach for regression testing. All these challenges related to effectiveness and cost, can be represented as measures of achieved code coverage and number of test cases created. A multiagent approach is proposed in this thesis since it allows the implementation of the autonomous behaviour and optimizes the data processing in a heterogeneous environment. In addition, the agent-based approach provides flexible design methods for building multitask applications and conducting parallel task execution. However, all of these advantages of using an agent-based approach need to be investigated in the regression testing domain for realistic scenarios. Therefore, a hypothesis was formulated in order to investigate the efficiency of the MTA approach using an experiment as the main research method for obtaining results. The thesis includes a comparison analysis between the MTA and well-known test case generation tools (i.e. EvoSuite and JUnitTools) for identifying the differences in terms of efficiency and code coverage achieved. The comparison results showed advantages of the MTA within regression testing context due to optimal level of code coverage and test cases. The outcome of the thesis work moves toward solving the aforementioned problems in regression testing domain and shows some advantages of using the multagent approach within regression testing context.

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NOGUEIRA, Yuri Lenon Barbosa. Integração mente e ambiente para a geração de comportamentos emergentes em personagens virtuais autônomos através da evolução de redes neurais artificiais. 2014. 112 f. Tese (Doutorado em ciência da computação)- Universidade Federal do Ceará, Fortaleza-CE, 2014. / Submitted by Elineudson Ribeiro (elineudsonr@gmail.com) on 2016-07-20T13:16:24Z No. of bitstreams: 1 2014_tese_ylbnogueira.pdf: 7506746 bytes, checksum: ff8ae576c5473bd3f6513dcbdee079eb (MD5) / Approved for entry into archive by Rocilda Sales (rocilda@ufc.br) on 2016-07-25T12:41:47Z (GMT) No. of bitstreams: 1 2014_tese_ylbnogueira.pdf: 7506746 bytes, checksum: ff8ae576c5473bd3f6513dcbdee079eb (MD5) / Made available in DSpace on 2016-07-25T12:41:47Z (GMT). No. of bitstreams: 1 2014_tese_ylbnogueira.pdf: 7506746 bytes, checksum: ff8ae576c5473bd3f6513dcbdee079eb (MD5) Previous issue date: 2014 / The user’s sense of immersion requires not only high visual quality of the virtual environment, but also accurate simulations of dynamics to ensure the reliability of the experience. In this context, the way the characters behave in a virtual environment plays a fundamental role. The problem that remains open is: “What needs to be done for autonomous virtual characters to display natural/realistic behaviors?”. A behavior is considered autonomous when the actions performed by the agent result from a close interaction between its internal dynamics and the circumstantial events in the environment, rather than from external control or specification dictated by a predefined plan. Thus, an autonomous behavior should reflect the details of the association between the character and its environment, resulting in greater naturalness and realistic movements. Therefore, it is proposed that the behavior is considered natural if it maintains coherence between the character’s body and the environment surrounding it. To an external observer, such coherence is perceived as intelligent behavior. This notion of intelligent behavior arose from a current debate, in the field of Artificial Intelligence, about the meaning of intelligence. Based on the new trends that came out from those discussions, it is argued that the level of coherence required for natural behavior in complex situations can only be achieved through emergence. In addition to the conceptual support of the emergentist approach to generating behavior of virtual characters, this study presents new techniques for implementing those ideas. A contribution of this work is a novel technique for the enconding and evolution of Artificial Neural Networks, which allows the development of controllers to explore the possibilities of generating behaviors through emergence. Evolution without objective description is also explored through the simulation of sexual reproduction of characters. In order to validate the theory, experiments involving a virtual robot were developed. The results show that self-organization of a system is indeed able to produce an intimate coupling between agent and environment. As a consequence of the adopted approach, it were achieved behaviors quite consistent with the character’s capabilities and environmental conditions, with or without description of objectives. The proposed methods were sensitive to changes in the environment and in the robot’s sensory apparatus, proving robustness on generating functional visual cortices, either with proximity sensors or with virtual cameras, interpreting its pixels. It is also emphasized the generation of different types of interesting behaviors, without any description of objectives, in experiments involving simulated reproduction. / O senso de imersão do usuário em um ambiente virtual requer não somente alta qualidade visual gráfica, mas também comportamentos adequados por parte dos personagens virtuais, isto é, com movimentos e ações que correspondam às suas características físicas e aos eventos que ocorrem em seu meio. Nesse contexto, percebe-se o papel fundamental desempenhado pelo modo como os agentes se comportam em aplicações de RV. O problema que permanece em aberto é: “Como obter comportamentos autônomos naturais e realistas de personagens virtuais?”. Um agente é dito autônomo se ele for capaz de gerar suas próprias normas (do grego autos, "a si mesmo", e nomos, "norma", "ordem"). Logo, autonomia implica em ações realizadas por um agente que resultam da estreita interação entre suas dinâmicas internas e os eventos ocorrendo no ambiente ao seu redor, ao invés de haver um controle externo ou uma especificação de respostas em um plano pré-definido. Desse modo, um comportamento autônomo deveria refletir os detalhes da associação entre o personagem e o ambiente, implicando em uma maior naturalidade e realismo nos movimentos. Assim, chega-se à proposta de que um comportamento é considerado natural se ele mantém coerência entre o corpo do personagem e o ambiente ao seu redor. Para um observador externo, tal coerência é percebida como comportamento inteligente. Essa noção resulta do atual debate, no campo da Inteligência Artificial, sobre o significado da inteligência. Baseado nas novas tendências surgidas dessas discussões, argumenta-se que o nível de coerência necessário a um comportamento natural apenas pode ser alcançado através de técnicas de emergência. Além da defesa conceitual da abordagem emergentista para a geração de comportamento de personagens virtuais, este estudo apresenta novas técnicas para a implementação dessas ideias. Entre as contribuições, está a proposta de um novo processo de codificação e evolução de Redes Neurais Artificiais que permite o desenvolvimento de controladores para explorar as possibilidades da geração de comportamentos por emergência. Também é explorada a evolução sem objetivo, através da simulação da reprodução sexuada de personagens. Para validar a tese, foram desenvolvidos experimentos envolvendo um robô virtual. Os resultados apresentados mostram que a auto-organização de um sistema é de fato capaz de produzir um acoplamento íntimo entre agente e ambiente. Como consequência da abordagem adotada, foram obtidos comportamentos bastante coerentes com as capacidades dos personagens e as condições ambientais, com ou sem descrição de objetivos. Os métodos propostos se mostraram sensíveis a modificações do ambiente e a modificações no sensoriamento do robô, comprovando robustez ao gerar córtices visuais funcionais, seja com sensores de proximidade, seja com câmeras virtuais, interpretando seus pixels. Ressalta-se também a geração de diferentes tipos de comportamentos interessantes, sem qualquer descrição de objetivos, nos experimentos envolvendo reprodução simulada.

Potential fields is popular technique in autonomous robotics. The Electric Field Approach is such a technique used for high level robot control. The approach was developed by Team Sweden for use in RoboCup 2000, a soccer competition for mobile robots. This thesis describes several improvements that has been made while preparing for RoboCup 2001. The improvements are used to create simple behaviors which achieve a form of implicit cooperation and also more active defence. This thesis also describes improvement ideas that failed to show the de-sired improvement and the reason for this is discussed, thereby deepening the understanding of the Electric Field Approach.

This thesis investigates the possibilities of explaining higher cognition as internal simulations of perception and action at an abstract level. Relatively recent findings in both neuroscience and psychology indicates that both perception and action can be internally simulated by activating sensory and motor areas in the brain in absence of sensory input and without any resulting overt behavior. An investigation was conducted in order to test the hypothesis that perception can be simulated in a mobile robot using abstractions. The result from this investigation showed that this was indeed the case but that the accuracy was limited. The simulations allowed the robot to anticipate long chains of future situations but were not good enough to support any overt behavior. To further improve the results there is a need for better training techniques and/or a more complex architecture.

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This work includes an attempt to enhance the autonomy of intelligent agents via real-time learning.In nature, the ability to learn at runtime gives species which can do so key advantages over others. While most AI systems do not need to have this ability but can be trained before deployment, it allows agents to adapt, at runtime, to changing and generally unknown circumstances, and then to exploit their environment for their own purposes. To reach this goal, in this thesis a pragmatic design (ONA) for a general-purpose reasoner incorporating Non-Axiomatic Reasoning System (NARS) theory is explored. The design and implementation is presented in detail, in addition to the theoretical foundation. Then, experiments related to various system capabilities are carried out and summarized, together with application projects where ONA is utilized: a traffic surveillance application in the Smart City domain to identify traffic anomalies through real-time reasoning and learning, and a system to help first responders by providing driving assistance and presenting of mission-critical information. Also it is shown how reliable real-time learning can help to increase autonomy of intelligent agents beyond the current state-of-the-art. Here, theoretical and practical comparisons with established frameworks and specific techniques such as Q-Learning are made, and it is shown that ONA does also work in non-Markovian environments where Q-Learning cannot be applied. Some of the reasoner's capabilities are also demonstrated on real robotic hardware. The experiments there show combining learning knowledge at runtime with the utilization of only partly complete mission-related background knowledge given by the designer, allowing the agent to perform a complex task from an only minimal mission specification which does not include learnable details. Overall, ONA is suitable for autonomous agents as it combines, in a single technique, the strengths of behavior learning, which is usually captured by Reinforcement Learning, and means-end reasoning (such as Belief-Desire-Intention models with planner) to effectively utilize knowledge expressed by a designer. / Computer and Information Science

Modern human societies give rise to the expression of complex group dynamics between the members of said society due to the abundance of continued interactions. Of particular interest are how institutions affect these interactions between societal members, alter the resulting group dynamics, and impact society as a whole through their rules. Simulating these dynamics allows for greater insight into how these institutions function and allows researchers to pose interesting questions and test hypotheses within a laboratory setting. We present a novel approach to simulating institutions, particularly governments, within a networked society. This approach builds upon the Junior High Game, which models a society of mixed motive individuals that are subject to reputation, network and power dynamics. In this work, we take a step towards evaluating the Junior High Game's ability to simulate societies with government institutions through evolutionary simulation. We evaluate the results through the simulated society's total welfare, equality, and group dynamics.

This thesis presents a framework for a mobile robot to escort a human to their destination successfully and efficiently. The proposed technique uses accurate path prediction incorporating human intention to locate the robot in front of the human while walking. Human intention is inferred by the head pose, an effective past-proven implicit indicator of intention, and fused with conventional physics-based motion prediction. The human trajectory is estimated and predicted using a particle filter because of the human's nonlinear and non-Gaussian behavior, and the robot control action is determined from the predicted human pose allowing for anticipative autonomous escorting. Experimental analysis shows that the incorporation of the proposed human intention model reduces human position prediction error by approximately 35% when turning. Furthermore, experimental validation with an omnidirectional mobile robotic platform shows escorting up to 50% more accurate compared to the conventional techniques, while achieving 97% success rate. / Master of Science / This thesis presents a method for a mobile robot to escort a human to their destination successfully and efficiently. The proposed technique uses human intention to predict the walk path allowing the robot to be in front of the human while walking. Human intention is inferred by the head direction, an effective past-proven indicator of intention, and is combined with conventional motion prediction. The robot motion is then determined from the predicted human position allowing for anticipative autonomous escorting. Experimental analysis shows that the incorporation of the proposed human intention reduces human position prediction error by approximately 35% when turning. Furthermore, experimental validation with an mobile robotic platform shows escorting up to 50% more accurate compared to the conventional techniques, while achieving 97% success rate. The unique escorting interaction method proposed has applications such as touch-less shopping cart robots, exercise companions, collaborative rescue robots, and sanitary transportation for hospitals.