Agent-based learning classifier systems for grid data mining

Grid Data Mining tools must be able to cope with very large, high dimensional and, frequently heterogeneous data sets that are geographically distributed and stored in different types of repositories, produced from different devices and retrieved through different protocols. This paper presents an agent-based version of a Learning Classifier System. An experimental study was conducted in a computer network in order to determine the systems’ efficiency. The results showed that the model is suitable to be applied in inherently distributed problems and is scalable, i.e., when the latency communication times are not considerable, the system obtains an interesting speedup

INTCare: a knowledge discovery based intelligent decision support system for intensive care medicine

This paper introduces the INTCare system, an intelligent information system based on a completely automated Knowledge Discovery process and on the Agents paradigm. The system was designed to work in Hospital Intensive Care Units, supporting the physicians’ decisions by means of prognostic Data Mining models. In particular, these techniques were used to predict organ failure and mortality assessment. The main intention is to change the current reactive behaviour to a pro-active one, enhancing the quality of service. Current applications and experimentations, the functional and structural aspects, and technological options are presented

Value Co-Creation in Smart Services: A Functional Affordances Perspective on Smart Personal Assistants

In the realm of smart services, smart personal assistants (SPAs) have become a popular medium for value co-creation between service providers and users. The market success of SPAs is largely based on their innovative material properties, such as natural language user interfaces, machine learning-powered request handling and service provision, and anthropomorphism. In different combinations, these properties offer users entirely new ways to intuitively and interactively achieve their goals and thus co-create value with service providers. But how does the nature of the SPA shape value co-creation processes? In this paper, we look through a functional affordances lens to theorize about the effects of different types of SPAs (i.e., with different combinations of material properties) on users’ value co-creation processes. Specifically, we collected SPAs from research and practice by reviewing scientific literature and web resources, developed a taxonomy of SPAs’ material properties, and performed a cluster analysis to group SPAs of a similar nature. We then derived 2 general and 11 cluster-specific propositions on how different material properties of SPAs can yield different affordances for value co-creation. With our work, we point out that smart services require researchers and practitioners to fundamentally rethink value co-creation as well as revise affordances theory to address the dynamic nature of smart technology as a service counterpart

Agente para recuperación automática de información en diversos entornos basado en técnicas de inteligencia computacional

Falta palabras clavesLa presente tesis se enmarca en la problemática de la recuperación de información, entendiendo por recuperación de información la búsqueda dentro de una colección de documentos diversos, de forma automática, de todos los documentos relacionados, con un cierto grado de relevancia, a partir de una consulta formulada por un usuario. En particular, expone un novedoso sistema, basado en lógica difusa, para la implementación de agentes inteligentes para resolver problemas reales de recuperación de información en diversos entornos. Los métodos de recuperación de información y de asignación de pesos desarrollados dan lugar a las publicaciones que se adjuntan en el compendio de esta tesis; y su aplicación propicia una entrada en la oficina de registro de la propiedad intelectual. En los trabajos de colaboración con empresa relacionados en el Capítulo 5 se han implementado diversos prototipos de agentes inteligentes aplicando las técnicas de inteligencia computacional que sustentan los métodos de recuperación de información desarrollados, con la finalidad de crear agentes inteligentes para resolución de problemas reales en los que se necesita realizar una recuperación de información. Los agentes inteligentes desarrollados utilizan el método de recuperación de información, el método de asignación de pesos, y la estructura de almacenamiento de información desarrollada en las publicaciones que forman el compendio de esta tesis. En dichas publicaciones se justifica el buen funcionamiento de estos métodos, así como la mejora de rendimiento en la recuperación de información contenida en portales web frente al modelo de espacio vectorial (Vector Space Model, VSM) y el método de asignación de pesos tf-idf

Persuasive and adaptive tutorial dialogues for a medical diagnosis tutoring system

The objective of this thesis is to address a key problem in the development of an intelligent tutoring system, that is, the implementation of the verbal exchange (a dialogue) that takes place between a student and the system. Here we consider TeachMed, a medical diagnosis tutoring system that teaches the students to diagnose clinical problems. However, approaches that are presented could also fit other tutoring systems. In such a system, a dialogue must be implemented that determines when and how pedagogic aid is provided to the student, that is, what to say to her, in what circumstances, and how to say it. Finite state machines and automated planning systems are so far the two most common approaches for implementing tutoring dialogues in intelligent tutoring systems. In the former approach, finite state machines of dialogues are manually designed and hard coded in intelligent tutoring systems. This is a straightforward but very time consuming approach. Furthermore, any change or extension to the hard coded finite state machines is very difficult as it requires reprogramming the system. On the other hand, automated planning has long been presented as a promising technique for automatic dialogue generating. However, in existing approaches, the requirement for the system to persuade the student is not formally acknowledged. Moreover, current dialogue planning approaches are not able to reason on uncertainties about the student's knowledge. This thesis presents two approaches for generating more effective tutorial dialogues.The first approach describes an argumentation framework for implementing persuasive tutoring dialogues. In this approach the entire interaction between the student and the tutoring system is seen as argumentation.The tutoring system and the student can settle conflicts arising during their argumentation by accepting, challenging, or questioning each other's arguments or withdrawing their own arguments. Pedagogic strategies guide the tutoring system by selecting arguments aimed at convincing the student.The second approach presents a non-deterministic planning technique which models the dialogue generation problem as one of planning with incomplete knowledge and sensing. This approach takes into account incomplete information about a particular fact of the student's knowledge by creating conditional branches in a dialogue plan such that each branch represents an adaptation of the dialogue plan with respect to a particular state of the student's knowledge or belief concerning the desired fact. In order to find out the real state of the student's knowledge and to choose the right branch at execution time, the planner includes some queries in the dialogue plan so that the tutoring system can ask the student to gather missing information. One contribution in this thesis is improving the quality of tutoring dialogues by engaging students in argumentative interactions and/or adapting the dialogues with respect to the student's knowledge. Another one is facilitating the design and implementation of tutoring by turning to automatically generated dialogues as opposed to manually generated ones

Comunicação humano-robô através de linguagem falada

Doutoramento em Engenharia ElectrotécnicaNos últimos anos, as tecnologias que dão suporte à robótica avançaram expressivamente. É possível encontrar robôs de serviço nos mais variados campos. O próximo passo é o desenvolvimento de robôs inteligentes, com capacidade de comunicação em linguagem falada e de realizar trabalhos úteis em interação/cooperação com humanos. Torna-se necessário, então, encontrar um modo de interagir eficientemente com esses robôs, e com agentes inteligentes de maneira geral, que permita a transmissão de conhecimento em ambos os sentidos. Partiremos da hipótese de que é possível desenvolver um sistema de diálogo baseado em linguagem natural falada que resolva esse problema. Assim, o objetivo principal deste trabalho é a definição, implementação e avaliação de um sistema de diálogo utilizável na interação baseada em linguagem natural falada entre humanos e agentes inteligentes. Ao longo deste texto, mostraremos os principais aspectos da comunicação por linguagem falada, tanto entre os humanos, como também entre humanos e máquinas. Apresentaremos as principais categorias de sistemas de diálogo, com exemplos de alguns sistemas implementados, assim como ferramentas para desenvolvimento e algumas técnicas de avaliação. A seguir, entre outros aspectos, desenvolveremos os seguintes: a evolução levada a efeito na arquitetura computacional do Carl, robô utilizado neste trabalho; o módulo de aquisição e gestão de conhecimento, desenvolvido para dar suporte à interação; e o novo gestor de diálogo, baseado na abordagem de “Estado da Informação”, também concebido e implementado no âmbito desta tese. Por fim, uma avaliação experimental envolvendo a realização de diversas tarefas de interação com vários participantes voluntários demonstrou ser possível interagir com o robô e realizar as tarefas solicitadas. Este trabalho experimental incluiu avaliação parcial de funcionalidades, avaliação global do sistema de diálogo e avaliação de usabilidade.In recent years, robotics-related technologies have reached a remarkable level of maturity. Service robots can be found in various fields. The next step is the development of intelligent robots, capable of communicating in spoken language and doing useful work in interaction / cooperation with humans. It is then necessary to find a way to efficiently interact with these robots, and with intelligent agents in general, enabling the transmission of knowledge in both directions. We will assume that one can develop a spoken language dialogue system to solve this problem. Therefore, the main goal of this work is the design, implementation and evaluation of a dialogue system that can be used on spoken language interaction between humans and intelligent agents. Throughout this document, we present and discuss the main aspects related to spoken language communication, among humans as well as between humans and machines. We present the main dialogue system categories, with examples of some implemented systems, development tools and a few evaluation techniques. Then, we describe the developed dialog system and its integration in a real robot, including the following aspects: the evolution in the computational architecture of Carl, the robot used in this work; the knowledge acquisition and management module, developed to support the interaction; and the new dialogue manager, based on the “Information State” approach, also designed and implemented within this thesis work. Finally, an experimental evaluation involving the completion of several interaction tasks involving several volunteers proved to be possible to interact with the robot and perform the requested tasks. The evaluation includes a partial evaluation of features, an overall evaluation of the dialogue system and a usability evaluation