Explanation-based learning for diagnosis

Diagnostic expert systems constructed using traditional knowledge-engineering techniques identify malfunctioning components using rules that associate symptoms with diagnoses. Model-based diagnosis (MBD) systems use models of devices to find faults given observations of abnormal behavior. These approaches to diagnosis are complementary. We consider hybrid diagnosis systems that include both associational and model-based diagnostic components. We present results on explanation-based learning (EBL) methods aimed at improving the performance of hybrid diagnostic problem solvers. We describe two architectures called EBL_IA and EBL(p). EBL_IA is a form fo "learning in advance" that pre-compiles models into associations. At run-time the diagnostic system is purely associational. In EBL(p), the run-time diagnosis system contains associational, MBD, and EBL components. Learned associational rules are preferred but when they are incomplete they may produce too many incorrect diagnoses. When errors cause performance to dip below a give threshold p, EBL(p) activates MBD and explanation-based "learning while doing". We present results of empirical studies comparing MBD without learning versus EBL_IA and EBL(p). The main conclusions are as follows. EBL_IA is superior when it is feasible but it is not feasible for large devices. EBL(p) can speed-up MBD and scale-up to larger devices in situations where perfect accuracy is not required

AI models and the future of genomic research and medicine: True sons of knowledge? Artificial intelligence needs to be integrated with causal conceptions in biomedicine to harness its societal benefits for the field

The increasing availability of large-scale, complex data has made research into how human genomes determine physiology in health and disease, as well as its application to drug development and medicine, an attractive field for artificial intelligence (AI) approaches. Looking at recent developments, we explore how such approaches interconnect and may conflict with needs for and notions of causal knowledge in molecular genetics and genomic medicine. We provide reasons to suggest that—while capable of generating predictive knowledge at unprecedented pace and scale—if and how these approaches will be integrated with prevailing causal concepts will not only determine the future of scientific understanding and self-conceptions in these fields. But these questions will also be key to develop differentiated policies, such as for education and regulation, in order to harness societal benefits of AI for genomic research and medicine

On Practical machine Learning and Data Analysis

This thesis discusses and addresses some of the difficulties associated with practical machine learning and data analysis. Introducing data driven methods in e.g industrial and business applications can lead to large gains in productivity and efficiency, but the cost and complexity are often overwhelming. Creating machine learning applications in practise often involves a large amount of manual labour, which often needs to be performed by an experienced analyst without significant experience with the application area. We will here discuss some of the hurdles faced in a typical analysis project and suggest measures and methods to simplify the process. One of the most important issues when applying machine learning methods to complex data, such as e.g. industrial applications, is that the processes generating the data are modelled in an appropriate way. Relevant aspects have to be formalised and represented in a way that allow us to perform our calculations in an efficient manner. We present a statistical modelling framework, Hierarchical Graph Mixtures, based on a combination of graphical models and mixture models. It allows us to create consistent, expressive statistical models that simplify the modelling of complex systems. Using a Bayesian approach, we allow for encoding of prior knowledge and make the models applicable in situations when relatively little data are available. Detecting structures in data, such as clusters and dependency structure, is very important both for understanding an application area and for specifying the structure of e.g. a hierarchical graph mixture. We will discuss how this structure can be extracted for sequential data. By using the inherent dependency structure of sequential data we construct an information theoretical measure of correlation that does not suffer from the problems most common correlation measures have with this type of data. In many diagnosis situations it is desirable to perform a classification in an iterative and interactive manner. The matter is often complicated by very limited amounts of knowledge and examples when a new system to be diagnosed is initially brought into use. We describe how to create an incremental classification system based on a statistical model that is trained from empirical data, and show how the limited available background information can still be used initially for a functioning diagnosis system. To minimise the effort with which results are achieved within data analysis projects, we need to address not only the models used, but also the methodology and applications that can help simplify the process. We present a methodology for data preparation and a software library intended for rapid analysis, prototyping, and deployment. Finally, we will study a few example applications, presenting tasks within classification, prediction and anomaly detection. The examples include demand prediction for supply chain management, approximating complex simulators for increased speed in parameter optimisation, and fraud detection and classification within a media-on-demand system

Plan de Emprendimiento Social con Apicultura, en el Departamento de Nariño – Colombia

No aplicaLos polinizadores entre ellas las abejas, son muy importantes para la vida integral en el planeta tierra. La apicultura como actividad agroecológica ofrece a la comunidad, productos ambientalmente sostenibles, nutricionales, con propiedades terapéuticas asociadas y además puede edificar socialmente en su ejercicio. Sin embargo, para el ejercicio apícola son varias las amenazas y son pocas las acciones efectivas que garantizan la sobrevivencia de esta actividad pecuaria y de estos insectos. Por ello, teniendo en cuenta la importancia de las acciones locales, esta investigación traza un eje de ruta para un emprendimiento social y asociativo en apicultura, aplicado al departamento de Nariño, Colombia, pudiendo ser adaptable a otras localidades. En ese sentido, esta investigación se desarrolla a partir de cuatro estudios. El primero, un estudio de mercado, en el cual se exalta a la miel de abejas, como el producto apícola más reconocido. El estudio de mercado identifica al cliente potencial, a través de la investigación con la salud humana, los alimentos y el mercado. También se incluyen estrategias de mercadeo explicadas a través del marketing mix tradicional. Segundo, estudio operacional, presenta el diagnóstico de la apicultura en el departamento de Nariño, basándose en datos del Instituto Colombiano Agropecuario (ICA), encuestas a apicultores, sondeos a miembros de la Asociación de Apicultores de Nariño (ASOAPINAR) y entrevista a una investigadora apícola de Nariño. Para finalizar este acápite, se presenta un análisis y descripción de actividades operativas. El tercer estudio es organizacional y revela un diagnóstico asociativo y un análisis del gremio apícola, presentando propuestas corporativas. Y el cuarto estudio es financiero y económico, en el cual se identifica el estimativo de inversión, ingresos e indicadores financieros para la idea de negocio. La investigación también incluye el análisis de la apicultura frente a: los objetivos de desarrollo sostenible (ODS), consideraciones políticas, factores económicos, realidades sociales, tendencias tecnológicas, argumentos ecológicos, determinaciones legales, perspectivas teóricas y nociones históricas. Incluyendo un enfoque social en los diferentes capítulos del proyecto. Este emprendimiento social, brinda herramientas para el sostenimiento de la apicultura, siendo básico el fortalecimiento asociativo y gremial de los apicultores y requiriendo de trabajo mancomunando interinstitucional.Pollinators, including bees, are very important for integral life on planet earth. Beekeeping as an agroecological activity offers the community, environmentally sustainable, nutritional products, with associated therapeutic properties and can also build socially in its exercise. However, for beekeeping there are several threats and there are few effective actions that guarantee the survival of this livestock activity and these insects. Therefore, considering the importance of local actions, this research traces a route axis for a social and associative enterprise in beekeeping, applied to el departamento de Nariño, Colombia, being able to be adaptable to other localities. In that sense, this research is developed from four studies. The first, a market study, in which honeybees is exalted, as the most recognized beekeeping product. The market study identifies the potential client, through research with human health, food, and market. Also included are marketing strategies explained through the traditional marketing mix. Second, operational study, presents the diagnosis of beekeeping in the department of Nariño, based on data from el Instituto Colombiano Agropecuario (ICA), surveys of beekeepers, surveys of members of la Asociación de Apicultores de Nariño (ASOAPINAR) and an interview with a beekeeping researcher from Nariño. To conclude this section, an analysis and description of operational activities is presented. The third study is organizational and reveals an associative diagnosis and an analysis of the beekeeping guild, presenting corporate proposals. And the fourth study is financial and economic, in which the estimated investment, income and financial indicators for the business idea is identified. The research also includes the analysis of beekeeping against the Sustainable Development Goals (SDGs), political considerations, economic factors, social realities, technological trends, ecological arguments, legal determinations, theoretical perspectives, and historical notions. Including a social approach in the different chapters of the project. This social enterprise provides tools for the support of beekeeping, being basic the associative and union strengthening of beekeepers and requiring inter-institutional joint work

Hearts and Minds: Mental Health Support for schools

Hearts and Minds is a collection of generic mental health case studies written by students at the University of Southern Queensland. The mental health concerns focus on those typically experienced within schools and include Anxiety, Autism Spectrum Disorder, Attention Deficit Hyperactivity Disorder, Depression, Post-Traumatic Stress Disorder and Suicidal Ideation

An intelligent clinical information management support system for the critical care medical environment

Significant advances have been achieved in the fields of medical informatics and artificial intelligence in medicine in the past three decades and, having demonstrated an ability to support clinical decisions, knowledge-based systems are becoming increasingly ubiquitous in various clinical settings. Nonetheless, few systems have so far been successful in entering routine use. On the one hand, primarily due to methodological difficulties and with very few exceptions, developers have failed to show that pertinent systems are effective in improving patient care. On the other hand, support systems have not been sufficiently well integrated into the routine information processing activity of the clinical users. As a consequence, their clinical utility is disputed and constructive assessmenist further hindered. This thesis describes the development of an intelligent clinical information management support system designed to overcome these obstacles through the adoption of an integrated approach, geared toward the solution of the problems encountered in the acquisition, organisation, review and interpretation of the clinical decision supporting information utilised in the process of monitoring intensive care unit patients with acid-base balance disorders. The system was developed to support this activity incrementally, using the methods of object-oriented analysis, design and implementation, with the active participation of a clinical advisor who assessed the functional and ergonomic compatibility of the system with the supported activity and the integration of a previously validated prototype knowledge-based data interpretation system, which could not evaluated in the clinical setting for the reasons described above

Erkennung und Behandlung von Ausnahmesituationen bei der Interaktion mit humanoiden Robotern

A diagnostic system is presented for the detection and recovery from exceptional situations that may occur in the cooperation of humanoid robots and humans in a rather complex environment. It is a hybrid system for a quick response to standard situations but still able to react to rather unknown exceptions. Already known standard errors are treated by associative diagnosis while for the remaining faults consistency-based techniques in diagnosis were transferred from a merely component oriented modelling of the robot to an operation oriented modelling of its actions as well. The actions had to be described as sequences of elementary operations that must fulfil a set of pre- and postconditions for a correct behaviour. These conditions may vary several times during the execution of a plan and may be time-dependent, especially for concurrent actions. The following recovery procedure defines the reaction to malfunction and strategies to delimit the effects of a failure and was done by a simple assignment for a start