This research has two facets within separate research areas. The research area of Belief, Desire and Intention (BDI) agent capability development was extended. Deception detection research has been advanced with the development of automation using BDI agents. BDI agents performed tasks automatically and autonomously. This study used these characteristics to automate deception detection with limited intervention of human users. This was a useful research area resulting in a capability general enough to have practical application by private individuals, investigators, organizations and others. The need for this research is grounded in the fact that humans are not very effective at detecting deception whether in written or spoken form. This research extends the deception detection capability research in that typical deception detection tools are labor intensive and require extraction of the text in question following ingestion into a deception detection tool. A neural network capability module was incorporated to lend the resulting prototype Machine Learning attributes. The prototype developed as a result of this research was able to classify online data as either "deceptive" or "not deceptive" with 85% accuracy. The false discovery rate for "deceptive" online data entries was 20% while the false discovery rate for "not deceptive" was 10%. The system showed stability during test runs. No computer crashes or other anomalous system behavior were observed during the testing phase. The prototype successfully interacted with an online data communications server database and processed data using Neural Network input vector generation algorithms within seconds

Approved for public release; distribution in unlimited. / The Army is undergoing a major realignment in accordance with the Joint Vision 2010/2020 transformation to establish an enterprise command that is the single authority to operate and manage the Army Enterprise Information Infrastructure (Infrastructure). However, there are a number of critical network management issues that the Army will have to overcome before attaining the full capabilities to manage the full spectrum of Army networks at the enterprise level. The Army network environment consists of an excessive number of heterogeneous applications, systems, and network architectures that are incompatible. There are a number of legacy systems and proprietary platforms. Most of the NM architectures in the Army are based on traditional centralized NM approaches such as the Simple Network Management Protocol (SNMP). Although SNMP is the most pervasive protocol, it lacks the scalability, reliability, flexibility and adaptability necessary to effectively support an enterprise network as large and complex as the Army. Attempting to scale these technologies to this magnitude can be extremely difficult and very costly. This thesis makes the argument that intelligent-agent-based technologies are a leading solution, among the other current technologies, to achieve the Army's enterprise network management goals. / Major, United States Army

[pt] Confiança é um aspecto fundamental em sistemas distribuídos abertos de larga-escala. Ela está no núcleo de todas as interações entre as entidades que precisam operar em ambientes com muita incerteza e que se modificam constantemente. Dada essa complexidade, esses componentes, e o sistema resultante, são cada vez mais contextualizados, desenhados e construídos usando técnicas baseadas em agentes. Portanto, confiança é fundamental em um sistema multi-agentes (MAS) aberto. Logo, este trabalho investiga como se ter um modelo de confiança explicitamente em um agente inteligente, que possui crenças (Beliefs), desejos (Desires) e intenções (Intentions), chamado de agente BDI. Ou seja, o agente passa a ter um quarto componente chamado confiança (Trust). Dessa forma, é necessário uma lógica para englobar o conceito de confiança em um MAS BDI aberto. Isso é feito usando uma lógica multi-modal indexada, onde os mundos possíveis que modelam um sistema multi-agentes representam quais agentes estão presentes em um dado instante de tempo. E, para cada uma três componentes originais de um agente BDI, há também uma representação de mundos possíveis, pois as mesmas são tratadas como modalidades. Já a confiança é modelada como sendo um predicado, e não uma modalidade. / [en] Trust is a fundamental concern in large-escale open distributed sytems. It lies at the core of all interactios between the entities that have to operate in such uncertain and constantly changing environmonts. Given the complexity of the interactions, these components, and the ensuing system, are increasingly being conceptualised, desined, and built using agent-based techiques. Therefore, the presence of trust is imperative in a multi-agent system (MAS). Consequently, this work studies how to have a explicit trust model in intelligent agent, which has beliefs, desires and intentions (BDI agent). Thas is, the agent now has a fourth component called Trust. This way, a logic to include the concept of trust in an open BDI MAS is interesting, so that the different aspects of a trust model can be expressed formally and accuratelly. This is achieved by using an indexed multi-modal logic, where the possible worlds which model a multi-agent system represent which agents are in the system in a given moment. Moreover, for each one of the three original components of a BDI agent, where the components represent beliefs, desires and intentions, there is a representation of possible worlds, because these are treated as modalities. However, trust is modelled as predicate, not as a modality.

[pt] Esta obra compreende a utilização de Máquinas de Estados Hierárquicas na definição e no controle de comportamento de agentes inteligentes para jogos eletrônicos. Esse tipo de Máquina de Estados permite reduções drásticas no número de transições quando comparada com uma Máquina de Estados não hierárquica que implemente o mesmo comportamento; além disso, oferece uma maneira mais intuitiva de se definir e entender representações gráficas de Máquinas de Estados compostas por muitos estados, típicas de agentes de jogos que necessitam de comportamento reativo complexo. / [en] This work comprehends the use of Hierarchical State Machines in the definition and control of behavior pertaining to intelligent agents in electronic games. This type of State Machine permits drastic reductions in the number of transitions when compared to a non-hierarchical State Machine that implements the same behavior; besides, it offers a more intuitive manner for defining and understanding graphical representations of State Machines consisting of many states, a typical scenario in game agents that need to exhibit complex reactive behavior.

<div>Over the past decade, universities had a significant improvement in using online learning tools. A standard learning management system provides fundamental functionalities to satisfy the basic needs of its users. The new generation of learning management systems have introduced a novel system that provides social networking features. An unprecedented number of users use the social aspects of such platforms to create their profile, collaborate with other users, and find their desired career path. Nowadays there are many learning systems which provide learning materials, certificates, and course management systems. This allows us to utilize such information to help the students and the instructors in their academic life. </div><div><br></div><div>The presented research work's primary goal is to focus on creating an intelligent personal assistant within the social learning systems. The proposed personal assistant has a human-like persona, learns about the users, and recommends useful and meaningful materials for them. The designed system offers a set of features for both institutions and members to achieve their goal within the learning system. It recommends jobs and friends for the users based on their profile. The proposed agent also prioritizes the messages and shows the most important message to the user. </div><div><br></div><div>The developed software supports model-controller-view architecture and provides a set of RESTful APIs which allows the institutions to integrate the proposed intelligent agent with their learning system. <br></div>

Since the realisation of the computer, and shortly after the inception of artificial intelligence (AI), there has been an explosion of research solving human-level tasks using autonomous entities that are able to learn about an environment by observing and influencing it, known as intelligent agents (IA). This potent AI technique has yet to filter into the field of thermoscience, where the conceptual design and optimisation of complex energy systems has been a particularly challenging problem. Much of the design process still requires human expertise. But with the continual increase in computational power and the use of IAs, it is now time to shift the responsibility from the human to the computer. This research attempts to answer the question of whether it is possible for a computer to conceptually design a complex energy system autonomously, from inception. The complex energy system to be designed and optimised is a thermoacoustic heat engine (TAHE), which converts thermal to acoustic power. The complexity of its physical behaviour and its many design parameters makes it a challenging energy system for conceptual design and optimisation and consequently an ideal candidate for this particular research. The TAHE is designed for low temperature waste heat utilisation from a baking process. In this work an approach is employed that is based on a reinforcement learning intelligent agent (RLIA). The RLIA is first employed to simultaneously optimise thirteen design parameter values. The RLIA was able to learn key design features of a TAHE which lead to the reduction in acoustic losses and an acoustic power from the engine of 495.32 W, when the thermal power input was 19 kW. For the main experiment, the RLIA must conceptually design the TAHE from scratch, changing both the parameter values and the configuration of the device. The results have shown the remarkable ability of the RLIA to identify several key design features of the TAHE: the correct configuration of the device, selecting designs that reduce acoustic losses, create positive acoustic power in the stack region and determine the region of optimality of the design parameter values. The RLIA has shown a great capacity to learn, even when contending with a complex environment and a vast search space. With this work we have introduced RLIAs as a new way approach to such multidimensional problems in the field of thermoscience/thermal engineering.

[pt] Esta tese investiga modelos híbridos neuro-fuzzy para aprendizado automático de ações efetuadas por agentes. O objetivo dos modelos é dotar um agente de inteligência, tornando-o capaz de, através da interação com o seu ambiente, adquirir e armazenar o conhecimento e raciocinar (inferir uma ação). O aprendizado desses modelos é realizado através de processo não-supervisionado denominado Aprendizado por Reforço (RL: Reinforcement Learning). Esta nova proposta de modelos neuro-fuzzy apresenta as seguintes características: aprendizado automático da estrutura do modelo; auto-ajuste dos parâmetros associados à estrutura; capacidade de aprender a ação a ser tomada quando o agente está em um determinado estado do ambiente; possibilidade de lidar com um número maior de entradas do que os sistemas neuro-fuzzy tradicionais; e geração de regras lingüísticas com hierarquia. O trabalho envolveu três etapas principais: levantamento bibliográfico e estudo de modelos de aprendizado; definição e implementação de dois novos modelos neuro-fuzzy hierárquicos baseados em RL; e estudo de casos. O levantamento bibliográfico e o estudo de modelos de aprendizado foi feito a partir dos modelos usados em agentes (com o objetivo de ampliar a ação autônoma) e em espaço de estados grande e/ou contínuo. A definição dos dois novos modelos neuro-fuzzy foi motivada pela importância de se estender a capacidade autônoma de agentes através do quesito inteligência, em particular a capacidade de aprendizado. Os modelos foram concebidos a partir do estudo das limitações existentes nos modelos atuais e das características desejáveis para sistemas de aprendizado baseados em RL, em particular quando aplicados a ambientes contínuos e/ou ambientes considerados de grande dimensão. Tais ambientes apresentam uma característica denominada curse of dimensionality que inviabiliza a aplicação direta de métodos tradicionais de RL. Assim sendo, a decisão de se usar uma metodologia de particionamento recursivo, já explorada com excelentes resultados em Souza (1999), que reduz significativamente as limitações dos sistemas neuro-fuzzy existentes, foi de fundamental importância para este trabalho. Optou-se pelos particionamentos BSP e Quadtree/Politree, gerando os dois modelos RL-NFHB (Reinforcement Learning - Neuro-Fuzzy Hierárquico BSP) e RL-NFHP (Reinforcement Learning - Neuro-Fuzzy Hierárquico Politree). Estes dois novos modelos são derivados dos modelos neuro-fuzzy hierárquicos NFHB e NFHQ (Souza, 1999) que utilizam aprendizado supervisionado. Com o uso desses métodos de particionamento, associados ao Reinforcement Learning, obteve-se uma nova classe de Sistemas Neuro-Fuzzy (SNF) que executam, além do aprendizado da estrutura, o aprendizado autônomo das ações a serem tomadas por um agente. Essas características representam um importante diferencial em relação aos sistemas de aprendizado de agentes inteligentes existentes. No estudo de casos, os dois modelos foram testados em 3 aplicações benckmark e uma aplicação em robótica. As aplicações benchmark são referentes a 3 problemas de sistemas de controle: o carro na montanha (mountain cart problem), estacionamento do carro (cart-centering problem) e o pêndulo invertido. A aplicação em robótica utilizou o modelo Khepera. A implementação dos modelos RL-NFHB e RL- NFHP foi feita em linguagem Java em microcomputadores com plataforma Windows 2000. Os testes efetuados demonstram que estes novos modelos se ajustam bem a problemas de sistemas de controle e robótica, apresentando boa generalização e gerando sua própria estrutura hierárquica de regras com interpretação lingüística. Além disso, o aprendizado automático do ambiente dota o agente de inteligência - (base de conhecimento, raciocínio e aprendizado), característica que aumenta a capacidade autônoma deste agente. A área de sistemas neuro-fuzzy hie / [en] This thesis investigates neuro-fuzzy hybrid models for automatic learning of actions taken by agents. The objective of these models is to provide an agent with intelligence, making it capable of acquiring and retaining knowledge and of reasoning (infer an action) by interacting with its environment. Learning in these models is performed by a non-supervised process, called Reinforcement Learning. These novel neuro-fuzzy models have the following characteristics: automatic learning of the model structure; auto-adjustment of parameters associated with the structure; capability of learning the action to be taken when the agent is on a given environment state; possibility of dealing with a larger number of inputs than those of traditional neuro-fuzzy systems; and the generation of hierarchical linguistic rules. This work comprised three main stages: bibliographic survey and study of learning models; definition and implementation of two new hierarchical neurofuzzy models based on Reinforcement Learning; and case studies. The bibliographic survey and the study of learning models considered learning models employed in agents (aiming to enhance the autonomous action) and in large and/or continuous state spaces. The definition of the two new neuro-fuzzy models was motivated by the importance of extending the autonomous capacity of agents through its intelligence, particularly the learning capacity. The models were conceived from the study of the existing limitations in current models, as well as the desirable characteristics for RL-based learning systems, particularly, when applied to continuous and/or high dimension environments. These environments present a characteristic called curse of dimensionality, which makes impracticable the direct application of the traditional RL- methods. Therefore, the decision of using a recursive partitioning methodology (already explored with excellent results in Souza, 1999), which significantly reduces the existing neuro-fuzzy systems limitations, was crucial to this work. The BSP (Binary Space Partitioning) and the Quadtree/Politree partitioning were then chosen, generating the RL-NFHB (Reinforcement Learning - Hierarchical Neuro- Fuzzy BSP) and RL-NFHP (Reinforcement Learning - Hierarchical Neuro-Fuzzy Politree) models. These two new models are derived from the hierarchical neuro-fuzzy models NFHB and NFHQ (Souza, 1999), which use supervised learning. By using these partitioning methods, together with the Reinforcement Learning methodology, a new class of Neuro-Fuzzy Systems (SNF) was obtained, which executes, in addition to structure learning, the autonomous learning of the actions to be taken by an agent. These characteristics represent an important differential when compared to the existing intelligent agents learning systems. In the case studies, the two models were tested in three benchmark applications and one application in robotics. The benchmark applications refer to 3 problems of control systems : the mountain cart problem, cart-centering problem, and the inverted pendulum. The application in robotics made use of the Khepera model. The RL-NFHB and RL-NFHP models were implemented using the Java language in Windows 2000 platform microcomputers. The experiments demonstrate that these new models are suitable for problems of control systems and robotics, presenting a good generalization and generating their own hierarchical structure of rules with linguistic interpretation. Moreover, the automatic environment learning endows the agent with intelligence (knowledge base, reasoning and learning). These are characteristics that increase the autonomous capacity of this agent. The hierarchical neuro-fuzzy systems field was also enhanced by the introduction of reinforcement learning, allowing the learning of hierarchical rules and actions to take place within the same process.

Submitted by Maicon Juliano Schmidt (maicons) on 2015-07-13T14:45:42Z No. of bitstreams: 1 Ederson Luiz Silveira.pdf: 2898339 bytes, checksum: 95165e769c65ef95ae0cfef8141a6d81 (MD5) / Made available in DSpace on 2015-07-13T14:45:42Z (GMT). No. of bitstreams: 1 Ederson Luiz Silveira.pdf: 2898339 bytes, checksum: 95165e769c65ef95ae0cfef8141a6d81 (MD5) Previous issue date: 2013-01-31 / UNISINOS - Universidade do Vale do Rio dos Sinos / A tecnologia de objetos de aprendizagem (OA) vêm tendo uma crescente utilização em contexto educacional, oferendo recursos digitais que estão cada vez mais presentes em salas de aula e na educação a distância. Em muitos casos, esta tecnologia está presente nos ambientes educacionais de forma implícita, sem que seja necessário saber que os materiais educacionais estão incorporados em objetos de aprendizagem. Como sua utilização cresce a cada ano, surge a necessidade de ferramentas que auxiliem o processo de catalogação e de edição dos metadados destes objetos, para tornar possível a sua recuperação, reutilização e alteração dos objetos. Para prover apoio a este processo de catalogação, a presente dissertação apresenta o sistema Linnaeus de suporte a catalogação de objetos de aprendizagem e edição de metadados, que através da junção das tecnologias de agentes inteligentes e da web semântica, na forma de wizards e de ontologias educacionais, se propõe a fornecer um apoio inteligente e pró-ativo, ajudando usuários sem conhecimentos técnicos sobre metadados ou padrões de objetos de aprendizagem a catalogar de forma correta seus objetos. / Learning objects (LO) are getting more importance in the education environment, providing digital resources that are increasingly common in both classrooms and e-learning teaching. Nowadays, these objects can be found in educational materials, without being explicitly defined as learning objects. During the latest years the use of learning objects is growing up, bringing with that the necessity of solutions for the cataloging and editing metadata information about these objects to make possible the recovery, reuse and even the change of these objects. To give support for this task, this dissertation proposes an intelligent learning object metadata editor, the Linnaeus system, which will provide help for the cataloging process of learning objects through the use of intelligent agents, and semantic web technologies, in the form of editing wizards and learning domain educational ontologies.

The recent emergence of intelligent agent technology and advances in information gathering have been the important steps forward in efficiently managing and using the vast amount of information now available on the Web to make informed decisions. There are, however, still many problems that need to be overcome in the information gathering research arena to enable the delivery of relevant information required by end users. Good decisions cannot be made without sufficient, timely, and correct information. Traditionally it is said that knowledge is power, however, nowadays sufficient, timely, and correct information is power. So gathering relevant information to meet user information needs is the crucial step for making good decisions. The ideal goal of information gathering is to obtain only the information that users need (no more and no less). However, the volume of information available, diversity formats of information, uncertainties of information, and distributed locations of information (e.g. World Wide Web) hinder the process of gathering the right information to meet the user needs. Specifically, two fundamental issues in regard to efficiency of information gathering are mismatch and overload. The mismatch means some information that meets user needs has not been gathered (or missed out), whereas, the overload means some gathered information is not what users need. Traditional information retrieval has been developed well in the past twenty years. The introduction of the Web has changed people's perceptions of information retrieval. Usually, the task of information retrieval is considered to have the function of leading the user to those documents that are relevant to his/her information needs. The similar function in information retrieval is to filter out the irrelevant documents (or called information filtering). Research into traditional information retrieval has provided many retrieval models and techniques to represent documents and queries. Nowadays, information is becoming highly distributed, and increasingly difficult to gather. On the other hand, people have found a lot of uncertainties that are contained in the user information needs. These motivate the need for research in agent-based information gathering. Agent-based information systems arise at this moment. In these kinds of systems, intelligent agents will get commitments from their users and act on the users behalf to gather the required information. They can easily retrieve the relevant information from highly distributed uncertain environments because of their merits of intelligent, autonomy and distribution. The current research for agent-based information gathering systems is divided into single agent gathering systems, and multi-agent gathering systems. In both research areas, there are still open problems to be solved so that agent-based information gathering systems can retrieve the uncertain information more effectively from the highly distributed environments. The aim of this thesis is to research the theoretical framework for intelligent agents to gather information from the Web. This research integrates the areas of information retrieval and intelligent agents. The specific research areas in this thesis are the development of an information filtering model for single agent systems, and the development of a dynamic belief model for information fusion for multi-agent systems. The research results are also supported by the construction of real information gathering agents (e.g., Job Agent) for the Internet to help users to gather useful information stored in Web sites. In such a framework, information gathering agents have abilities to describe (or learn) the user information needs, and act like users to retrieve, filter, and/or fuse the information. A rough set based information filtering model is developed to address the problem of overload. The new approach allows users to describe their information needs on user concept spaces rather than on document spaces, and it views a user information need as a rough set over the document space. The rough set decision theory is used to classify new documents into three regions: positive region, boundary region, and negative region. Two experiments are presented to verify this model, and it shows that the rough set based model provides an efficient approach to the overload problem. In this research, a dynamic belief model for information fusion in multi-agent environments is also developed. This model has a polynomial time complexity, and it has been proven that the fusion results are belief (mass) functions. By using this model, a collection fusion algorithm for information gathering agents is presented. The difficult problem for this research is the case where collections may be used by more than one agent. This algorithm, however, uses the technique of cooperation between agents, and provides a solution for this difficult problem in distributed information retrieval systems. This thesis presents the solutions to the theoretical problems in agent-based information gathering systems, including information filtering models, agent belief modeling, and collection fusions. It also presents solutions to some of the technical problems in agent-based information systems, such as document classification, the architecture for agent-based information gathering systems, and the decision in multiple agent environments. Such kinds of information gathering agents will gather relevant information from highly distributed uncertain environments.

ITC/USA 2012 Conference Proceedings / The Forty-Eighth Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2012 / Town and Country Resort & Convention Center, San Diego, California / A major intelligence challenge we face in today's national security environment is the threat of terrorist attack against our national assets, especially our citizens. This paper addresses global reconnaissance which incorporates an autonomous Intelligent Agent/Data Fusion solution for recognizing potential risk of terrorist attack through identifying and reporting imminent persona-oriented terrorist threats based on data reduction/compression of a large volume of low latency data possibly from hundreds, or even thousands of data points.