CoordenaÃÃo de AperfeÃoamento de Pessoal de NÃvel Superior / O senso de imersÃo do usuÃrio em um ambiente virtual requer nÃo somente alta qualidade visual grÃfica, mas tambÃm comportamentos adequados por parte dos personagens virtuais, isto Ã, com movimentos e aÃÃes que correspondam Ãs suas caracterÃsticas fÃsicas e aos eventos que ocorrem em seu meio. Nesse contexto, percebe-se o papel fundamental desempenhado pelo modo como os agentes se comportam em aplicaÃÃes de RV. O problema que permanece em aberto Ã: âComo obter comportamentos autÃnomos naturais e realistas de personagens virtuais?â. Um agente à dito autÃnomo se ele for capaz de gerar suas prÃprias normas (do grego autos, "a si mesmo", e nomos, "norma", "ordem"). Logo, autonomia implica em aÃÃes realizadas por um agente que resultam da estreita interaÃÃo entre suas dinÃmicas internas e os eventos ocorrendo no ambiente ao seu redor, ao invÃs de haver um controle externo ou uma especificaÃÃo de respostas em um plano prÃ-definido. Desse modo, um comportamento autÃnomo deveria refletir os detalhes da associaÃÃo entre o personagem e o ambiente, implicando em uma maior naturalidade e realismo nos movimentos. Assim, chega-se à proposta de que um comportamento à considerado natural se ele mantÃm coerÃncia entre o corpo do personagem e o ambiente ao seu redor. Para um observador externo, tal coerÃncia à percebida como comportamento inteligente. Essa noÃÃo resulta do atual debate, no campo da InteligÃncia Artificial, sobre o significado da inteligÃncia. Baseado nas novas tendÃncias surgidas dessas discussÃes, argumenta-se que o nÃvel de coerÃncia necessÃrio a um comportamento natural apenas pode ser alcanÃado atravÃs de tÃcnicas de emergÃncia. AlÃm da defesa conceitual da abordagem emergentista para a geraÃÃo de comportamento de personagens virtuais, este estudo apresenta novas tÃcnicas para a implementaÃÃo dessas ideias. Entre as contribuiÃÃes, està a proposta de um novo processo de codificaÃÃo e evoluÃÃo de Redes Neurais Artificiais que permite o desenvolvimento de controladores para explorar as possibilidades da geraÃÃo de comportamentos por emergÃncia. TambÃm à explorada a evoluÃÃo sem objetivo, atravÃs da simulaÃÃo da reproduÃÃo sexuada de personagens. Para validar a tese, foram desenvolvidos experimentos envolvendo um robà virtual. Os resultados apresentados mostram que a auto-organizaÃÃo de um sistema à de fato capaz de produzir um acoplamento Ãntimo entre agente e ambiente. Como consequÃncia da abordagem adotada, foram obtidos comportamentos bastante coerentes com as capacidades dos personagens e as condiÃÃes ambientais, com ou sem descriÃÃo de objetivos. Os mÃtodos propostos se mostraram sensÃveis a modificaÃÃes do ambiente e a modificaÃÃes no sensoriamento do robÃ, comprovando robustez ao gerar cÃrtices visuais funcionais, seja com sensores de proximidade, seja com cÃmeras virtuais, interpretando seus pixels. Ressalta-se tambÃm a geraÃÃo de diferentes tipos de comportamentos interessantes, sem qualquer descriÃÃo de objetivos, nos experimentos envolvendo reproduÃÃo simulada. / The userâs 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: âWhat needs to be done for autonomous virtual characters to display natural/realistic behaviors?â. 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 characterâs 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 characterâs capabilities and environmental conditions, with or without description of objectives. The proposed methods were sensitive to changes in the environment and in the robotâs 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.

Behavioral animation has become popular for creating virtual characters that are autonomous agents and thus self-animating. This is useful for lessening the workload of human animators, populating virtual environments with interactive agents, etc. Unfortunately, current behavioral animation techniques suffer from three key problems: (1) deliberative behavioral models (i.e., cognitive models) are slow to execute; (2) interactive virtual characters cannot adapt online due to interaction with a human user; (3) programming of behavioral models is a difficult and time-intensive process. This dissertation presents a collection of papers that seek to overcome each of these problems. Specifically, these issues are alleviated through novel machine learning schemes. Problem 1 is addressed by using fast regression techniques to quickly approximate a cognitive model. Problem 2 is addressed by a novel multi-level technique composed of custom machine learning methods to gather salient knowledge with which to guide decision making. Finally, Problem 3 is addressed through programming-by-demonstration, allowing a non technical user to quickly and intuitively specify agent behavior.

Cognitive and behavioral models have become popular methods to create autonomous self-animating characters. Creating these models presents the following challenges: (1) Creating a cognitive or behavioral model is a time intensive and complex process that must be done by an expert programmer (2) The models are created to solve a specific problem in a given environment and because of their specific nature cannot be easily reused. Combining existing models together would allow an animator, without the need of a programmer, to create new characters in less time and would be able to leverage each model's strengths to increase the character's performance, and to create new behaviors and animations. This thesis provides a framework that can aggregate together existing behavioral and cognitive models into an ensemble. An animator only has to rate how appropriately a character performed and through machine learning the system is able to determine how the character should act given the current situation. Empirical results from multiple case studies validate the approach taken.