To cope with dynamics and uncertainties, a novel penalty function-based hybrid, multi-agent shop floor control system is proposed in this dissertation. The key characteristic of the proposed system is the capability of adaptively distributing decision-making power across different levels of control agents in response to different levels of disturbance. The subordinate agent executes tasks based on the schedule from the supervisory level agent in the absence of disturbance. Otherwise, it optimizes the original schedule before execution by revising it with regard to supervisory level performance (via penalty function) and disturbance. Penalty function, mathematical programming formulations, and quantitative metrics are presented to indicate the disturbance levels and levels of autonomy. These formulations are applied to diverse performance measurements such as completion time related metrics, makespan, and number of late jobs. The proposed control system is illustrated, tested with various job shop problems, and benchmarked against other shop floor control systems. In today's manufacturing system, man still plays an important role together with the control system Therefore, better coordination of humans and control systems is an inevitable topic. A novel BDI agent-based software model is proposed in this work to replace the partial decision-making function of a human. This proposed model is capable of 1) generating plans in real-time to adapt the system to a changing environment, 2) supporting not only reactive, but also proactive decision-making, 3) maintaining situational awareness in human language-like logic to facilitate real human decision-making, and 4) changing the commitment strategy adaptive to historical performance. The general purposes human operator model is then customized and integrated with an automated shop floor control system to serve as the error detection and recovery system. This model has been implemented in JACK software; however, JACK does not support real-time generation of a plan. Therefore, the planner sub-module has been developed in Java and then integrated with the JACK. To facilitate integration of an agent, real-human, and the environment, a distributed computing platform based on DOD High Level Architecture has been used. The effectiveness of the proposed model is then tested in several scenarios in a simulated automated manufacturing environment.

In der vorliegenden Untersuchung sollte ein möglicher Zusammenhang zwischen mundgesundheitsbezogener Lebensqualität (MLQ) und Depression untersucht und bei prothetischen Patienten und Personen der Allgemeinbevölkerung vergleichend betrachtet werden. Es handelt sich um eine Querschnittsstudie, in welche 311 zahnärztlich-prothetische Patienten und 811 erwachsene Probanden der Bundesrepublik Deutschland einbezogen wurden. Zur Erfassung von MLQ und Depression wurden als standardisierte Instrumente zum einen die deutsche Version des Oral Health Impact Profile (OHIP) und zum anderen das Vereinfachte-Beck-Depressions-Inventar (BDI-V) eingesetzt. Die statistischen Zusammenhänge der Konstrukte wurden mit Hilfe von Korrelationsanalysen sowie Strukturgleichungsmodellen ermittelt. Die konfundierenden Variablen Alter und Geschlecht wurden dabei kontrolliert. In beiden Populationen wurde ein signifikanter Zusammenhang zwischen Depression und MLQ festgestellt. Diese gegenseitige Beeinflussung zwischen MLQ sowie deren Dimensionen und der Depression war jedoch zwischen den beiden Probandengruppen signifikant unterschiedlich stark ausgeprägt. Bei den prothetischen Patienten war der Zusammenhang nur halb so stark ausgeprägt wie bei den Personen der Allgemeinbevölkerung. Die OHIP-Dimension „Psychosozialer Einfluss“ der MLQ korrelierte in beiden Probandengruppen signifikant stärker mit Depression als die anderen Dimensionen. Folglich könnte sie auf eine mögliche Depression hinweisen und als zahnärztliches Diagnostikum eingesetzt werden. Zudem legt diese Studie nahe, dass eine eingeschränkte MLQ möglicherweise erst zeitversetzt zu depressiven Symptomen führen kann.

La principal contribución de esta Tesis es la propuesta de un modelo de agente BDI graduado (g-BDI) que permita especificar una arquitetura de agente capaz de representar y razonar con actitudes mentales graduadas. Consideramos que una arquitectura BDI más exible permitirá desarrollar agentes que alcancen mejor performance en entornos inciertos y dinámicos, al servicio de otros agentes (humanos o no) que puedan tener un conjunto de motivaciones graduadas. En el modelo g-BDI, las actitudes graduadas del agente tienen una representación explícita y adecuada. Los grados en las creencias representan la medida en que el agente cree que una fórmula es verdadera, en los deseos positivos o negativos permiten al agente establecer respectivamente, diferentes niveles de preferencias o de rechazo. Las graduaciones en las intenciones también dan una medida de preferencia pero en este caso, modelan el costo/beneficio que le trae al agente alcanzar una meta. Luego, a partir de la representación e interacción de estas actitudes graduadas, pueden ser modelados agentes que muestren diferentes tipos de comportamiento. La formalización del modelo g-BDI está basada en los sistemas multi-contextos. Diferentes lógicas modales multivaluadas se han propuesto para representar y razonarsobre las creencias, deseos e intenciones, presentando en cada caso una axiomática completa y consistente. Para tratar con la semántica operacional del modelo de agente, primero se definió un calculus para la ejecución de sistemas multi-contextos, denominado Multi-context calculus. Luego, mediante este calculus se le ha dado al modelo g-BDI semántica computacional. Por otra parte, se ha presentado una metodología para la ingeniería de agentes g-BDI en un escenario multiagente. El objeto de esta propuesta es guiar el diseño de sistemas multiagentes, a partir de un problema del mundo real. Por medio del desarrollo de un sistema recomendador en turismo como caso de estudio, donde el agente recomendador tiene una arquitectura g-BDI, se ha mostrado que este modelo es valioso para diseñar e implementar agentes concretos. Finalmente, usando este caso de estudio se ha realizado una experimentación sobre la flexibilidad y performance del modelo de agente g-BDI, demostrando que es útil para desarrollar agentes que manifiesten conductas diversas. También se ha mostrado que los resultados obtenidos con estos agentes recomendadores modelizados con actitudes graduadas, son mejores que aquellos alcanzados por los agentes con actitudes no-graduadas. / The central contribution of this dissertation is the proposal of a graded BDI agent model (g-BDI), specifying an architecture capable of representing and reasoning with graded mental attitudes. We consider that making the BDI architecture more exible will allow us to design and develop agents capable of improved performance in uncertain and dynamic environments, serving other agents (human or not) that may have a set of graded motivations.In the g-BDI model, the agent graded attitudes have an explicit and suitable representation. Belief degrees represent the extent to which the agent believes a formula to be true. Degrees of positive or negative desires allow the agent to set di_erent levels of preference or rejection respectively. Intention degrees also give a preference measure but, in this case, modelling the cost/benefit trade off of achieving an agent's goal. Then, agents having different kinds of behaviour can be modelled on the basis of the representation and interaction of their graded attitudes. The formalization of the g-BDI agent model is based on Multi-context systems and in order to represent and reason about the beliefs, desires and intentions, we followed a many-valued modal approach. Also, a sound and complete axiomatics for representing each graded attitude is proposed. Besides, in order to cope with the operational semantics aspects of the g-BDI agent model, we first defined a Multi-context calculus for Multi-context systems execution and then, using this calculus we give this agent model computational meaning.Furthermore, a software engineering process to develop graded BDI agents in a multiagent scenario is presented. The aim of the proposed methodology is to guide the design of a multiagent system starting from a real world problem. Through the development of a Tourism recommender system, where one of its principal agents is modelled as a g-BDI agent, we show that the model is useful to design and implement concrete agents.Finally, using the case study we have made some experiments concerning the exibility and performance of the g-BDI agent model, demonstrating that this agent model is useful to develop agents showing varied and rich behaviours. We also show that the results obtained by these particular recommender agents using graded attitudes improve those achieved by agents using non-graded attitudes.

Personal assistants need to allow the user to interact with the system in a flexible and adaptive way such as through spoken language dialogue. This research is aimed at achieving robust and effective dialogue management in such applications. We focus on an application, the Smart Personal Assistant (SPA), in which the user can use a variety of devices to interact with a collection of personal assistants, each specializing in a task domain. The current implementation of the SPA contains an e-mail management agent and a calendar agent that the user can interact with through a spoken dialogue and a graphical interface on PDAs. The user-system interaction is handled by a Dialogue Manager agent. We propose an agent-based approach that makes use of a BDI agent architecture for dialogue modelling and control. The Dialogue Manager agent of the SPA acts as the central point for maintaining coherent user-system interaction and coordinating the activities of the assistants. The dialogue model consists of a set of complex but modular plans for handling communicative goals. The dialogue control flow emerges automatically as the result of the agent???s plan selection by the BDI interpreter. In addition the Dialogue Manager maintains the conversational context, the domainspecific knowledge and the user model in its internal beliefs. We also consider the problem of dialogue adaptation in such agent-based dialogue systems. We present a novel way of integrating learning into a BDI architecture so that the agent can learn to select the most suitable plan among those applicable in the current context. This enables the Dialogue Manager agent to tailor its responses according to the conversational context and the user???s physical context, devices and preferences. Finally, we report the evaluation results, which indicate the robustness and effectiveness of the dialogue model in handling a range of users.

Personal assistants need to allow the user to interact with the system in a flexible and adaptive way such as through spoken language dialogue. This research is aimed at achieving robust and effective dialogue management in such applications. We focus on an application, the Smart Personal Assistant (SPA), in which the user can use a variety of devices to interact with a collection of personal assistants, each specializing in a task domain. The current implementation of the SPA contains an e-mail management agent and a calendar agent that the user can interact with through a spoken dialogue and a graphical interface on PDAs. The user-system interaction is handled by a Dialogue Manager agent. We propose an agent-based approach that makes use of a BDI agent architecture for dialogue modelling and control. The Dialogue Manager agent of the SPA acts as the central point for maintaining coherent user-system interaction and coordinating the activities of the assistants. The dialogue model consists of a set of complex but modular plans for handling communicative goals. The dialogue control flow emerges automatically as the result of the agent???s plan selection by the BDI interpreter. In addition the Dialogue Manager maintains the conversational context, the domainspecific knowledge and the user model in its internal beliefs. We also consider the problem of dialogue adaptation in such agent-based dialogue systems. We present a novel way of integrating learning into a BDI architecture so that the agent can learn to select the most suitable plan among those applicable in the current context. This enables the Dialogue Manager agent to tailor its responses according to the conversational context and the user???s physical context, devices and preferences. Finally, we report the evaluation results, which indicate the robustness and effectiveness of the dialogue model in handling a range of users.

Personal assistants need to allow the user to interact with the system in a flexible and adaptive way such as through spoken language dialogue. This research is aimed at achieving robust and effective dialogue management in such applications. We focus on an application, the Smart Personal Assistant (SPA), in which the user can use a variety of devices to interact with a collection of personal assistants, each specializing in a task domain. The current implementation of the SPA contains an e-mail management agent and a calendar agent that the user can interact with through a spoken dialogue and a graphical interface on PDAs. The user-system interaction is handled by a Dialogue Manager agent. We propose an agent-based approach that makes use of a BDI agent architecture for dialogue modelling and control. The Dialogue Manager agent of the SPA acts as the central point for maintaining coherent user-system interaction and coordinating the activities of the assistants. The dialogue model consists of a set of complex but modular plans for handling communicative goals. The dialogue control flow emerges automatically as the result of the agent???s plan selection by the BDI interpreter. In addition the Dialogue Manager maintains the conversational context, the domainspecific knowledge and the user model in its internal beliefs. We also consider the problem of dialogue adaptation in such agent-based dialogue systems. We present a novel way of integrating learning into a BDI architecture so that the agent can learn to select the most suitable plan among those applicable in the current context. This enables the Dialogue Manager agent to tailor its responses according to the conversational context and the user???s physical context, devices and preferences. Finally, we report the evaluation results, which indicate the robustness and effectiveness of the dialogue model in handling a range of users.

A representação do conhecimento de maneira mais fiel possível à realidade é uma meta histórica e não resolvida até o momento na área da Inteligência Artificial. Problemas são resolvidos e decisões são tomadas levando-se em conta diversos tipos de conhecimentos, os quais muitos são tendenciosos, inexatos, ambíguos ou ainda incompletos. A fim de tentar emular a capacidade de representação do conhecimento humano, levando-se em conta as diversas dificuldades inerentes, tem-se construído sistemas computacionais que armazenam o conhecimento das mais diversas formas. Dentro deste contexto, este trabalho propõe um experimento que utiliza duas formas distintas de representação do conhecimento: a simbólica, neste caso BDI, e a probabilística, neste caso Redes Bayesianas. Para desenvolvermos uma prova de conceito desta proposta de representação do conhecimento estamos utilizando exemplos que serão construídos através da tecnologia de programação voltada para agentes. Para tal, foi desenvolvida uma implementação de um Sistema MultiAgente, estendendo o framework Jason através da implementação de um plugin chamado COPA. Para a representação do conhecimento probabilístico, utilizamos uma ferramenta de construção de Redes Bayesianas, também adaptada a este sistema. Os estudos de caso mostraram melhorias no gerenciamento do conhecimento incerto em relação às abordagens de construções de agentes BDI clássicos, ou seja, que não utilizam conhecimento probabilístico. / Achieving faithful representation of knowledge is a historic and still unreached goal in the area of Artificial Intelligence. Problems are solved and decisions are made taking into consideration different kinds of knowledge, from which many are biased, inaccurate, ambiguous or still incomplete. Computational systems that store knowledge in many different ways have been built in order to emulate the capacity of human knowledge representation, taking into consideration the several inherent difficulties to it. Within this context, this paper proposes an experiment that utilizes two distinct ways of representing knowledge: symbolic, BDI in this case, and probabilistic, Bayesian Networks in this case. In order to develop a proof of concept of this propose of knowledge representation, examples that will be built through agent oriented programming technology will be used. For that, implementation of a MultiAgent System was developed, extending the Jason framework through the implementation of a plugin called COPA. For the representation of probabilistic knowledge, a Bayesian Network building tool, also adapted to this system, was used. The case studies showed improvement in the management of uncertain knowledge in relation to the building approaches of classic BDI agents, i.e., that do not use probabilistic knowledge.

A tecnologia de agentes surge como uma solução que fornece flexibilidade e robustez para lidar com domínios dinâmicos e complexos. Tal flexibilidade pode ser alcançada através da adoção de abordagens já existentes baseadas em agentes, como a arquitetura BDI, que provê agentes com características mentais de crenças, desejos e intenções. Essa arquitetura é altamente personalizável, deixando lacunas a serem preenchidas de acordo com aplicações específicas. Uma dessas lacunas é o algoritmo de seleção de planos, responsável por selecionar um plano para ser executado pelo agente buscando atingir um objetivo, e tendo grande influência no desempenho geral do agente. Grande parte das abordagens existentes requerem considerável esforço para personalização e ajuste a fim de serem utilizadas em aplicações específicas. Nessa dissertação, propomos uma abordagem para seleção de planos apta a aprender quais planos possivelmente terão os melhores resultados, baseando-se no contexto atual e nas preferências do agente. Nossa abordagem é composta por um meta-modelo, que deve ser instanciado a fim de especificar metadados de planos, e uma técnica que usa tais metadados para aprender e predizer resultados da execução destes planos. Avaliamos nossa abordagem experimentalmente e os resultados indicam que ela é efetiva. Adicionalmente, fornecemos uma ferramenta para apoiar o processo de desenvolvimento de agentes de software baseados em nosso trabalho. Essa ferramenta permite que desenvolvedores modelem e gerem código-fonte para agentes BDI com capacidades de aprendizado. Um estudo com usuários foi realizado para avaliar os benefícios de um método de desenvolvimento baseado em agentes BDI auxiliado por ferramenta. Evidências sugerem que nossa ferramenta pode auxiliar desenvolvedores que não sejam especialistas ou que não estejam familiarizados com a tecnologia de agentes. / Agent technology arises as a solution that provides flexibility and robustness to deal with dynamic and complex domains. Such flexibility can be achieved by the adoption of existing agent-based approaches, such as the BDI architecture, which provides agents with the mental attitudes of beliefs, desires and intentions. This architecture is highly customisable, leaving gaps to be fulfilled in particular applications. One of these gaps is the plan selection algorithm that is responsible for selecting a plan to be executed by an agent to achieve a goal, having an important influence on the overall agent performance. Most existing approaches require considerable effort for customisation and adjustment to be used in particular applications. In this dissertation, we propose a plan selection approach that is able to learn plans that provide possibly best outcomes, based on current context and agent’s preferences. Our approach is composed of a meta-model, which must be instantiated to specify plan metadata, and a technique that uses such metadata to learn and predict plan outcomes. We evaluated our approach experimentally, and results indicate it is effective. Additionally, we provide a tool to support the development process of software agents based on our work. This tool allows developers to model and generate source code for BDI agents with learning capabilities. A user study was performed to assess the improvements of a tool-supported BDI-agent-based development method, and evidences suggest that our tool can help developers that are not experts or are unfamiliar with the agent technology.

A representação do conhecimento de maneira mais fiel possível à realidade é uma meta histórica e não resolvida até o momento na área da Inteligência Artificial. Problemas são resolvidos e decisões são tomadas levando-se em conta diversos tipos de conhecimentos, os quais muitos são tendenciosos, inexatos, ambíguos ou ainda incompletos. A fim de tentar emular a capacidade de representação do conhecimento humano, levando-se em conta as diversas dificuldades inerentes, tem-se construído sistemas computacionais que armazenam o conhecimento das mais diversas formas. Dentro deste contexto, este trabalho propõe um experimento que utiliza duas formas distintas de representação do conhecimento: a simbólica, neste caso BDI, e a probabilística, neste caso Redes Bayesianas. Para desenvolvermos uma prova de conceito desta proposta de representação do conhecimento estamos utilizando exemplos que serão construídos através da tecnologia de programação voltada para agentes. Para tal, foi desenvolvida uma implementação de um Sistema MultiAgente, estendendo o framework Jason através da implementação de um plugin chamado COPA. Para a representação do conhecimento probabilístico, utilizamos uma ferramenta de construção de Redes Bayesianas, também adaptada a este sistema. Os estudos de caso mostraram melhorias no gerenciamento do conhecimento incerto em relação às abordagens de construções de agentes BDI clássicos, ou seja, que não utilizam conhecimento probabilístico. / Achieving faithful representation of knowledge is a historic and still unreached goal in the area of Artificial Intelligence. Problems are solved and decisions are made taking into consideration different kinds of knowledge, from which many are biased, inaccurate, ambiguous or still incomplete. Computational systems that store knowledge in many different ways have been built in order to emulate the capacity of human knowledge representation, taking into consideration the several inherent difficulties to it. Within this context, this paper proposes an experiment that utilizes two distinct ways of representing knowledge: symbolic, BDI in this case, and probabilistic, Bayesian Networks in this case. In order to develop a proof of concept of this propose of knowledge representation, examples that will be built through agent oriented programming technology will be used. For that, implementation of a MultiAgent System was developed, extending the Jason framework through the implementation of a plugin called COPA. For the representation of probabilistic knowledge, a Bayesian Network building tool, also adapted to this system, was used. The case studies showed improvement in the management of uncertain knowledge in relation to the building approaches of classic BDI agents, i.e., that do not use probabilistic knowledge.

A tecnologia de agentes surge como uma solução que fornece flexibilidade e robustez para lidar com domínios dinâmicos e complexos. Tal flexibilidade pode ser alcançada através da adoção de abordagens já existentes baseadas em agentes, como a arquitetura BDI, que provê agentes com características mentais de crenças, desejos e intenções. Essa arquitetura é altamente personalizável, deixando lacunas a serem preenchidas de acordo com aplicações específicas. Uma dessas lacunas é o algoritmo de seleção de planos, responsável por selecionar um plano para ser executado pelo agente buscando atingir um objetivo, e tendo grande influência no desempenho geral do agente. Grande parte das abordagens existentes requerem considerável esforço para personalização e ajuste a fim de serem utilizadas em aplicações específicas. Nessa dissertação, propomos uma abordagem para seleção de planos apta a aprender quais planos possivelmente terão os melhores resultados, baseando-se no contexto atual e nas preferências do agente. Nossa abordagem é composta por um meta-modelo, que deve ser instanciado a fim de especificar metadados de planos, e uma técnica que usa tais metadados para aprender e predizer resultados da execução destes planos. Avaliamos nossa abordagem experimentalmente e os resultados indicam que ela é efetiva. Adicionalmente, fornecemos uma ferramenta para apoiar o processo de desenvolvimento de agentes de software baseados em nosso trabalho. Essa ferramenta permite que desenvolvedores modelem e gerem código-fonte para agentes BDI com capacidades de aprendizado. Um estudo com usuários foi realizado para avaliar os benefícios de um método de desenvolvimento baseado em agentes BDI auxiliado por ferramenta. Evidências sugerem que nossa ferramenta pode auxiliar desenvolvedores que não sejam especialistas ou que não estejam familiarizados com a tecnologia de agentes. / Agent technology arises as a solution that provides flexibility and robustness to deal with dynamic and complex domains. Such flexibility can be achieved by the adoption of existing agent-based approaches, such as the BDI architecture, which provides agents with the mental attitudes of beliefs, desires and intentions. This architecture is highly customisable, leaving gaps to be fulfilled in particular applications. One of these gaps is the plan selection algorithm that is responsible for selecting a plan to be executed by an agent to achieve a goal, having an important influence on the overall agent performance. Most existing approaches require considerable effort for customisation and adjustment to be used in particular applications. In this dissertation, we propose a plan selection approach that is able to learn plans that provide possibly best outcomes, based on current context and agent’s preferences. Our approach is composed of a meta-model, which must be instantiated to specify plan metadata, and a technique that uses such metadata to learn and predict plan outcomes. We evaluated our approach experimentally, and results indicate it is effective. Additionally, we provide a tool to support the development process of software agents based on our work. This tool allows developers to model and generate source code for BDI agents with learning capabilities. A user study was performed to assess the improvements of a tool-supported BDI-agent-based development method, and evidences suggest that our tool can help developers that are not experts or are unfamiliar with the agent technology.