"Consider two people having a face-to-face conversation. They sometimes listen, sometimes talk, and sometimes interrupt each other. They use facial expressions to signal that they are confused. They point at objects. They jump from topic to topic opportunistically. When another acquaintance walks by, they nod and say hello. All the while they have other concerns on their mind, such as not missing the meeting that starts in 10 minutes. Like many other humans behaviors, these are not easy to replicate in artificial agents. In this work we look into the design requirements of an embodied agent that can participate in such natural conversations in a mixed-initiative, multi-modal setting. Such an agent needs to understand participating in a conversation is not merely a matter of sending a message and then waiting to receive a response -- both partners are simultaneously active at all times. This agent should be able to deal with different, sometimes conflicting goals, and be always ready to address events that may interrupt the current topic of conversation. To address those requirements, we have created a modular architecture that includes distributed functional units that compete with each other to gain control over available resources. Each of these units, called a schema, has its own sense- think-act cycle. In the field of robotics, this design is often referred to as "behavior-based" or "schema-based." The major contribution of this work is merging behavior-based robotics with plan- based human-computer interaction."

Robotic science can be defined as a modern multi-disciplinary branch of science, which hosts many technological elements with a huge theoretic base. From electrical and electronics engineering point of view, construction of intelligent agents that produce and/or collects information by interacting the surrounding environment and that can achieve some goal via learning, is investigated in robotic science. In this scope, behavior-based robotic control has emerged in recent years, which can be defined as a hierarchically higher control mechanism over classical control theory and applications. In this thesis, software which is capable of producing behavior-based control over mobile robots is constructed. Research encapsulates an investigation on behavior-based robotic concept by comparison of different approaches. Although there are numerous commercial and freeware software products for robotics, the number of open source, detail documented software on behavior-based control concept together with easy usage is limited. Aimed to fulfill a necessity in this field, an open source software environment is implemented in which different algorithms and applications can be developed. In order to evaluate the effectiveness and the capabilities of the implemented software, a fully detailed simulation is conducted. This simulation covers multi-behavior coordination concept for a differential drive mobile robot navigating in a collision free path through a target point which is detected by sensors, in an unstructured environment, that robot has no priori information about, in which static and moving obstacles exists. Coordination is accomplished by artificial neural network with back-propagation training algorithm. Behaviors are constructed using fuzzy control concept. Mobile robot has no information about sizes, number of static and/or dynamic obstacles. All the information is gathered by its simulated sensors (proximity, range, vision sensors). Yielded results are given in detail.

This thesis focuses on developing of a distributed, efficient and fault tolerant multiresolutional architecture for sensor networks. For demonstrative purpose, a powerful simulation environment using 3D environment model has been developed. The robot network is composed of autonomous robots capable of working cooperatively equipped with single typed simple sensor. The developed layered control architecture is hybrid including both subsumption and motor schema control strategies. In this proposed control method, behaviors in different or in same layer are coordinated with an evaluator unit that overcomes the difficulties of subsumption based architectures in terms of behavioral coordination. The final coordination between these layers is achieved cooperatively. We performed many simulation experiments to test robot deployment, search and task execution. It is shown that some important parameters such as target reaching time, energy consumption, and communication range can be optimized if an approximate prior information about the environment is known. Robots executes task based on a task allocation algorithm. Market based auction method is used as a task allocation algorithm with completely different robot fitness evaluation method allowing a distributive problem solving. Six non-linear fitness functions are developed to increase the fairness, and fault tolerance of task allocation. These functions have been tested to represent the successes and failures of robots in a compact form. Performance analyses test results have shown that fairness increases two times more in task allocation when these fitness functions are used, compared to the results existing fitness evaluation methods used in the market based auction algorithms. Moreover, fault tolerance is increased by using fitness functions devoted to failure conditions.

Orientadores: Fernando Jose Von Zuben, Mauricio Fernandes Figueiredo / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-06T19:35:31Z (GMT). No. of bitstreams: 1 Michelan_Roberto_M.pdf: 4495515 bytes, checksum: aed72feefc89070579190e862ea0f740 (MD5) Previous issue date: 2006 / Resumo: A concepção de sistemas autônomos de navegação para robôs móveis, havendo múltiplos objetivos simultâneos a serem atendidos, como a coleta de lixo com manutenção da integridade, requer a adoção de técnicas refinadas de coordenação de módulos de comportamento elementar. Modelos de redes imunológicas artificiais podem então ser empregados na proposição de um controlador concebido com base em um processo de mapeamento dinâmico. Os anticorpos da rede são responsáveis pelos módulos de comportamento elementar, na forma de regras do tipo <condição>-<ação>, e as conexões são responsáveis pelos mecanismos de estímulo e supressão entre os anticorpos. A rede iniciará uma resposta imunológica sempre que lhe forem apresentados os antígenos. Estes antígenos representam a situação atual capturada pelos sensores do robô. A dinâmica da rede é baseada no nível de concentração dos anticorpos, definida com base na interação dos anticorpos e dos anticorpos com os antígenos. De acordo com o nível de concentração, um anticorpo é escolhido para definir a ação do robô. Um processo evolutivo é então responsável por definir um padrão de conexões para a rede imunológica, a partir de uma população de redes candidatas, capaz de maximizar o atendimento dos objetivos durante a navegação. Resulta então um sistema híbrido que tem a rede imunológica como responsável por introduzir um processo dinâmico de tomada de decisão e tem agora a computação evolutiva como responsável por definir a estrutura da rede. Para que fosse possível avaliar os controladores (redes imunológicas) a cada geração do processo evolutivo, um ambiente virtual foi desenvolvido para simulação computacional, com base nas características do problema de navegação. As redes imunológicas obtidas através do processo evolutivo foram analisadas e testadas em novas situações, apresentando capacidade de coordenação em tarefas simples e complexas. Os experimentos preliminares com um robô real do tipo Khepera II indicaram a eficácia da ferramenta de navegação / Abstract: The design of an autonomous navigation system for mobile robots, with simultaneous objectives to be satisfied, as garbage collection with maintenance of integrity, requires refined coordination mechanisms to deal with modules of elementary behavior. Models of artificial immune networks can then be applied to produce a controller based on dynamic mapping. The antibodies of the immune network are responsible for the modules of elementary behavior, in the form of <condition>-<action> rules, and the connections are responsible for the mechanisms of stimulation and suppression of antibodies. The network will always start an immune response when antigens are presented. These antigens represent the current output of the robot sensors. The network dynamics is based on the levels of antibody concentration, provided by interaction among antibodies, and among antibodies and antigens. Based on its concentration level, an antibody is chosen to define the robot action. An evolutionary process is then used to define the connection pattern of the immune network, from a population of candidate networks, capable of maximizing the objectives during navigation. As a consequence, a hybrid system is conceived, with an immune network implementing a dynamic process of decision-making, and an evolutionary algorithm defining the network structure. To be able to evaluate the controllers (immune networks) at each iteration of the evolutionary process, a virtual environment was developed for computer simulation, based on the characteristics of the navigation problem. The immune networks obtained by evolution were analyzed and tested in new situations and presented coordination capability in simple and complex tasks. The preliminary experiments on a real Khepera II robot indicated the efficacy of the navigation tool / Mestrado / Engenharia de Computação / Mestre em Engenharia Elétrica