Symbolic Explanation of Similarities in Case-based Reasoning

CBR systems solve problems by assessing their similarity with already solved problems (cases). Explanation of a CBR system prediction usually consists of showing the user the set of cases that are most similar to the current problem. Examining those retrieved cases the user can then assess whether the prediction is sensible. Using the notion of symbolic similarity, our proposal is to show the user a symbolic description that makes explicit what the new problem has in common with the retrieved cases. Specifically, we use the notion of anti-unification (least general generalization) to build symbolic similarity descriptions. We present an explanation scheme using anti-unification for CBR systems applied to classification tasks. This scheme focuses on symbolically describing what is shared between the current problem and the retrieved cases that belong to different classes. Examining these descriptions of symbolic similarities the user can assess which aspects are determining that a problem is classified one way or another. The paper exemplifies this proposal with an implemented application of the symbolic similarity scheme to the domain of predicting the carcinogenic activity of chemical compounds

Aprender com o Passado: Apoio à Negociação Automática nos Centros de Controlo Operacionais

O processo de planeamento e escalonamento dos voos de uma companhia aérea é composto por várias fases, algumas das quais começam a ser preparadas com vários meses de antecedência. Contudo, é tão importante ter um planeamento ótimo quanto conseguir mantê-lo. Esta é uma tarefa bastante complicada devido a eventos inesperados (roturas) que ocorrem perto do dia da operação. Tais problemas originam quebras no planeamento que podem levar a atrasos e/ou cancelamento de voos, se nada for feito para o prevenir.No Laboratório de Inteligência Artificial e Ciência da Computação (LIACC) está a ser desenvolvido um projeto, denominado Multi-Agent System for Disruption Management (MASDIMA), em colaboração com a TAP Portugal, no âmbito da negociação automática na Resolução Distri- buída de Problemas em ambientes Cooperativos, aplicado ao cenário de Controlo Operacional das Companhias Aéreas, através de um sistema multiagente de gestão de roturas.O objetivo desta dissertação é o de incorporar, no sistema MASDIMA, uma camada de software suplementar, no conjunto de agentes responsáveis pela geração, análise e decisão de novas soluções, para que este sistema possa aprender com o passado. É assim estudada a possibilidade de o sistema gerar novas soluções para os problemas atuais com base no seu conhecimento de situações similares ocorridas anteriormente.Para que esta implementação seja uma realidade, utilizar-se-á a metodologia Case-based Reasoning (CBR), introduzindo desta forma a aprendizagem com o passado no apoio à negociação automática nos Centros de Controlo Operacionais, para obter a solução final para a rotura do plano.Pretende-se, nesta dissertação, que a introdução da aprendizagem com o passado, no sistema MASDIMA, mantenha, aproximadamente, a qualidade das soluções apresentadas pelo sistema ao mesmo tempo que, por um lado, se diminui o tempo médio de resposta do sistema a um novo problema, e, por outro, se aumenta o nível de confiabilidade do mesmo. A concretização destes objetivos será analisada através da utilização de métricas como o tempo médio de resposta do sistema a um novo caso, e a determinação da qualidade das soluções propostas, em comparação com as soluções dadas pelo Centro de Controlo Operacional (CCO) da TAP Portugal, e com as produzidas pelas abordagens pré-existentes no sistema MASDIMA.The process of planning and scheduling the flights of an airline consists of several steps, some of which are prepared several months in advance. Even though, having a great plan is as important as keeping it, this task can be quite demanding due to unexpected events (disruptions) that can occur close to the day of operation. Such problems can lead to delays and / or cancellation of flights, if nothing is done to prevent it.In the Laboratório de Inteligência Artificial e Ciência da Computação (LIACC) is being developed a project called Multi-Agent System for Disruption Management (MASDIMA), in collabo- ration with TAP Portugal, as part of an automatic negotiation on Cooperative Distributed Problem Solving (CDPS), applied to the scenario of Airlines Operation Control Center (AOCC) through a multi-agent system for disruption management.The aim of this dissertation is to incorporate, in MASDIMA system, an additional software layer, on the set of agents responsible by the generation, analysis and decision regarding new solutions, so they can learn from the past. Therefore, it is being investigated a way of having the system solve current problems based on its knowledge of similar situations occurred in the past and already solved.In order for this to become a reality, it will be used Case-based Reasoning (CBR). Using this methodology, we will be able to resolve problems, learning from the past, on the AOCC.The intention in this dissertation is to show that the introduction of learning from the past in MASDIMA system will maintain the quality of the solutions presented, and, at the same time, decrease the average response time of the system to a new problem and increase its trust. The achievement of these objectives will be analyzed using metrics such as the average response time of the system to a new case, and determining the quality of the proposed solutions, i.e. comparing the results to previously produced solutions by the system MASDIMA and TAP actual AOC

Integrating case-based planning and RPTW neural networks to construct an intelligent environment for health care

This paper presents an intelligent environment developed for monitoring patients’ health care in execution time in hospital environments. The CBPMP (case-based planner for monitoring patients) is an autonomous deliberative case-based planner designed to plan the nurses’ working time dynamically, to maintain the standard working reports about the nurses’ activities, and to guarantee that the patients assigned to the nurses are given the right care. The planner operates in wireless devices and is integrated with complementary software into an intelligent environment, named AmI-P (Ambient Intelligence for patients). CBPMP description, its relationship with the complementary technology, and preliminary results of system prototype in a real environment are presented

A dynamic adaptive framework for improving case-based reasoning system performance

An optimal performance of a Case-Based Reasoning (CBR) system means, the CBR system must be efficient both in time and in size, and must be optimally competent. The efficiency in time is closely related to an efficient and optimal retrieval process over the Case Base of the CBR system. Efficiency in size means that the Case Library (CL) size should be minimal. Therefore, the efficiency in size is closely related to optimal case learning policies, optimal meta-case learning policies, optimal case forgetting policies, etc. On the other hand, the optimal competence of a CBR system means that the number of problems that the CBR system can satisfactorily solve must be maximum. To improve or optimize all three dimensions in a CBR system at the same time is a difficult challenge because they are interrelated, and it becomes even more difficult when the CBR system is applied to a dynamic or continuous domain (data stream). In this thesis, a Dynamic Adaptive Case Library framework (DACL) is proposed to improve the CBR system performance coping especially with reducing the retrieval time, increasing the CBR system competence, and maintaining and adapting the CL to be efficient in size, especially in continuous domains. DACL learns cases and organizes them into dynamic cluster structures. The DACL is able to adapt itself to a dynamic environment, where new clusters, meta-cases or prototype of cases, and associated indexing structures (discriminant trees, k-d trees, etc.) can be formed, updated, or even removed. DACL offers a possible solution to the management of the large amount of data generated in an unsupervised continuous domain (data stream). In addition, we propose the use of a Multiple Case Library (MCL), which is a static version of a DACL, with the same structure but being defined statically to be used in supervised domains. The thesis work proposes some techniques for improving the indexation and the retrieval task. The most important indexing method is the NIAR k-d tree algorithm, which improves the retrieval time and competence, compared against the baseline approach (a flat CL) and against the well-known techniques based on using standard k-d tree strategies. The proposed Partial Matching Exploration (PME) technique explores a hierarchical case library with a tree indexing-structure aiming at not losing the most similar cases to a query case. This technique allows not only exploring the best matching path, but also several alternative partial matching paths to be explored. The results show an improvement in competence and time of retrieving of similar cases. Through the experimentation tests done, with a set of well-known benchmark supervised databases. The dynamic building of prototypes in DACL has been tested in an unsupervised domain (environmental domain) where the air pollution is evaluated. The core task of building prototypes in a DACL is the implementation of a stochastic method for the learning of new cases and management of prototypes. Finally, the whole dynamic framework, integrating all the main proposed approaches of the research work, has been tested in simulated unsupervised domains with several well-known databases in an incremental way, as data streams are processed in real life. The conclusions outlined that from the experimental results, it can be stated that the dynamic adaptive framework proposed (DACL/MCL), jointly with the contributed indexing strategies and exploration techniques, and with the proposed stochastic case learning policies, and meta-case learning policies, improves the performance of standard CBR systems both in supervised domains (MCL) and in unsupervised continuous domains (DACL).El rendimiento óptimo de un sistema de razonamiento basado en casos (CBR) significa que el sistema CBR debe ser eficiente tanto en tiempo como en tamaño, y debe ser competente de manera óptima. La eficiencia temporal está estrechamente relacionada con que el proceso de recuperación sobre la Base de Casos del sistema CBR sea eficiente y óptimo. La eficiencia en tamaño significa que el tamaño de la Base de Casos (CL) debe ser mínimo. Por lo tanto, la eficiencia en tamaño está estrechamente relacionada con las políticas óptimas de aprendizaje de casos y meta-casos, y las políticas óptimas de olvido de casos, etc. Por otro lado, la competencia óptima de un sistema CBR significa que el número de problemas que el sistema puede resolver de forma satisfactoria debe ser máximo. Mejorar u optimizar las tres dimensiones de un sistema CBR al mismo tiempo es un reto difícil, ya que están relacionadas entre sí, y se vuelve aún más difícil cuando se aplica el sistema de CBR a un dominio dinámico o continuo (flujo de datos). En esta tesis se propone el Dynamic Adaptive Case Library framework (DACL) para mejorar el rendimiento del sistema CBR especialmente con la reducción del tiempo de recuperación, aumentando la competencia del sistema CBR, manteniendo y adaptando la CL para ser eficiente en tamaño, especialmente en dominios continuos. DACL aprende casos y los organiza en estructuras dinámicas de clusters. DACL es capaz de adaptarse a entornos dinámicos, donde los nuevos clusters, meta-casos o prototipos de los casos, y las estructuras asociadas de indexación (árboles discriminantes, árboles k-d, etc.) se pueden formar, actualizarse, o incluso ser eliminados. DACL ofrece una posible solución para la gestión de la gran cantidad de datos generados en un dominio continuo no supervisado (flujo de datos). Además, se propone el uso de la Multiple Case Library (MCL), que es una versión estática de una DACL, con la misma estructura pero siendo definida estáticamente para ser utilizada en dominios supervisados. El trabajo de tesis propone algunas técnicas para mejorar los procesos de indexación y de recuperación. El método de indexación más importante es el algoritmo NIAR k-d tree, que mejora el tiempo de recuperación y la competencia, comparado con una CL plana y con las técnicas basadas en el uso de estrategias de árboles k-d estándar. Partial Matching Exploration (PME) technique, la técnica propuesta, explora una base de casos jerárquica con una indexación de estructura de árbol con el objetivo de no perder los casos más similares a un caso de consulta. Esta técnica no sólo permite explorar el mejor camino coincidente, sino también varios caminos parciales alternativos coincidentes. Los resultados, a través de la experimentación realizada con bases de datos supervisadas conocidas, muestran una mejora de la competencia y del tiempo de recuperación de casos similares. Además la construcción dinámica de prototipos en DACL ha sido probada en un dominio no supervisado (dominio ambiental), donde se evalúa la contaminación del aire. La tarea central de la construcción de prototipos en DACL es la implementación de un método estocástico para el aprendizaje de nuevos casos y la gestión de prototipos. Por último, todo el sistema, integrando todos los métodos propuestos en este trabajo de investigación, se ha evaluado en dominios no supervisados simulados con varias bases de datos de una manera gradual, como se procesan los flujos de datos en la vida real. Las conclusiones, a partir de los resultados experimentales, muestran que el sistema de adaptación dinámica propuesto (DACL / MCL), junto con las estrategias de indexación y de exploración, y con las políticas de aprendizaje de casos estocásticos y de meta-casos propuestas, mejora el rendimiento de los sistemas estándar de CBR tanto en dominios supervisados (MCL) como en dominios continuos no supervisados (DACL).Postprint (published version

Book Review. - Literatur

Book Review. - Literatu

Medical Education for the 21st Century

Medical education has undergone a substantial transformation from the traditional models of the basic classroom, laboratory, and bedside that existed up to the late 20th century. The focus of this text is to review the spectrum of topics that are essential to the training of 21st-century healthcare providers. Modern medical education goes beyond learning physiology, pathophysiology, anatomy, pharmacology, and how they apply to patient care. Contemporary medical education models incorporate multiple dimensions, including digital information management, social media platforms, effective teamwork, emotional and coping intelligence, simulation, as well as advanced tools for teaching both hard and soft skills. Furthermore, this book also evaluates the evolving paradigm of how teachers can teach and how students can learn – and how the system evaluates success

Desafios da tradução técnica : manuais de segurança e policiamento

Este relatório é fruto do trabalho desenvolvido durante o estágio profissionalizante realizado no âmbito do mestrado em tradução da Faculdade de Letras da Universidade de Lisboa durante o ano lectivo 2017/2018. O estágio teve lugar no Instituto de Ciências Policiais e Segurança Interna, no período compreendido entre Setembro de 2017 e Julho de 2018. Mediante a aplicação dos conhecimentos adquiridos nos seminários de tradução científica/técnica, foi desenvolvido o trabalho de tradução de manuais respeitantes a medidas de policiamento e de segurança aplicáveis em diferentes tipos de situações. Este relatório procura dar a conhecer as principais questões de tradução, as estratégias utilizadas e a análise crítica das soluções apresentadas.This report come as the product of the activity undertaken throughout the professional internship within the scope of the master’s degree in translation, delivered by the Faculdade de Letras da Universidade de Lisboa, in the time span of 2017-2018. Bearing in mind the competences obtained during the translation seminars, a translation work was carried out, focused on security and policing guidance which is applicable to different types of scenarios. This report aims to expose the challenges underlying the translation, the strategies used and the critical analysis of the presented solutions

Unmet goals of tracking: within-track heterogeneity of students' expectations for

Educational systems are often characterized by some form(s) of ability grouping, like tracking. Although substantial variation in the implementation of these practices exists, it is always the aim to improve teaching efficiency by creating homogeneous groups of students in terms of capabilities and performances as well as expected pathways. If students’ expected pathways (university, graduate school, or working) are in line with the goals of tracking, one might presume that these expectations are rather homogeneous within tracks and heterogeneous between tracks. In Flanders (the northern region of Belgium), the educational system consists of four tracks. Many students start out in the most prestigious, academic track. If they fail to gain the necessary credentials, they move to the less esteemed technical and vocational tracks. Therefore, the educational system has been called a 'cascade system'. We presume that this cascade system creates homogeneous expectations in the academic track, though heterogeneous expectations in the technical and vocational tracks. We use data from the International Study of City Youth (ISCY), gathered during the 2013-2014 school year from 2354 pupils of the tenth grade across 30 secondary schools in the city of Ghent, Flanders. Preliminary results suggest that the technical and vocational tracks show more heterogeneity in student’s expectations than the academic track. If tracking does not fulfill the desired goals in some tracks, tracking practices should be questioned as tracking occurs along social and ethnic lines, causing social inequality

Aeronautical engineering: A continuing bibliography with indexes (supplement 267)

This bibliography lists 661 reports, articles, and other documents introduced into the NASA scientific and technical information system in June, 1991. Subject coverage includes design, construction and testing of aircraft and aircraft engines; aircraft components, equipment and systems; ground support systems; theoretical and applied aspects of aerodynamics and general fluid dynamics; electrical engineering; aircraft control; remote sensing; computer sciences; nuclear physics; and social sciences