Expert system application education project

Artificial intelligence (AI) technology, and in particular expert systems, has shown potential applicability in many areas of operation at the Kennedy Space Center (KSC). In an era of limited resources, the early identification of good expert system applications, and their segregation from inappropriate ones can result in a more efficient use of available NASA resources. On the other hand, the education of students in a highly technical area such as AI requires an extensive hands-on effort. The nature of expert systems is such that proper sample applications for the educational process are difficult to find. A pilot project between NASA-KSC and the University of Central Florida which was designed to simultaneously address the needs of both institutions at a minimum cost. This project, referred to as Expert Systems Prototype Training Project (ESPTP), provided NASA with relatively inexpensive development of initial prototype versions of certain applications. University students likewise benefit by having expertise on a non-trivial problem accessible to them at no cost. Such expertise is indispensible in a hands-on training approach to developing expert systems

The Intellectual Training Environment for Prolog Programming Language

In this work is described a new complex training system, named SPprolog, intended for training and self-training in logic programming language - Prolog. This system includes elements related to Prolog and logic programming, and the elements of independent, complex, self-sufficient training system which is capable considerably to increase the quality of self-training, and to be effective assistant in training. The most useful application of the system can be in distance education and self-training. The main elements of SPprolog system are: Functionally expanded (in comparison with existing systems) Prolog development environment, with the multipurpose code editor, the automated organization system of the personal tools, automated advice mode "Expert Advice", based on the incorporated expert system for cultivated, effective and optimized programming; Link to foreign Prolog programs compiler which allow to compile the program to independent executable; Built in intellectual, interactive, multimedia Prolog interpreter integrated with expert system and the elements of the intellectuality, allowing to lead detailed program interpretation, with popular and evident, explanation of the theory and mechanisms used in it, applying audiovisual effects to increase the level of naturalness of process of explanation; Full digital training course of Prolog programming language presented in the form of the matrix of knowledge and supplied system of consecutive knowledge reproduction for self-training and evaluation; an intensive course of training to the Prolog language and Spprolog system, based on the programmed, consecutive set of actions, allowing using the previous two mechanisms of sys-tem for popular and evident explanation of the main principles of work of system and Prolog language.training, prolog, environment, Spprolog

Analysis of expert players’ training process: Validation of tools

El objetivo del presente estudio fue la creación y validación de una batería de instrumentos de medición (ATPEP), compuesta por dos entrevistas y una escala, para identificar y analizar el proceso de formación en jugadores expertos de deportes colectivos. La batería de instrumentos se ha elaborado siguiendo un procedimiento ecléctico (inductivo y deductivo). En el proceso de validación se utilizó el peritaje de expertos con 11 jueces, calculando la validez de contenido mediante la obtención del coeficiente V de Aiken. Las dimensiones abordadas en las entrevista y en la escala fueron: contexto social, contexto deportivo, habilidades interindividuales, habilidades intraindividuales, táctica, técnica, condición física y antropometría. Solamente 2 de los 132 ítems evaluados obtuvieron valores de V lo suficientemente bajos como para ser eliminados. La propuesta final de la batería de instrumentos ATPEP se ha elaborado teniendo en cuenta las valoraciones cualitativas y cuantitativas de los expertosThe aim of this study was to create and validate a string of measurement tools (ATPEP), composed of two interviews and one scale, in order to identify and analyze expert team players’ training process. This string of tools has been elaborated using an eclectic procedure (inductive and deductive).11 experts judges led the validation process, and calculating content validity by obtaining Aiken´s V coefficient. The discussed dimensions in both the interviews and the scale were the following: social context, sport context, inter-individual abilities, intra-individual abilities, tactic, technique, fitness and anthropometry. Only 2 out of the 132 evaluated items obtained V values low enough to be removed. Finally, the ATPEP battery instruments were elaborated taking into account quantitative and qualitative experts’ assessments.Este trabajo ha sido parcialmente subvencionado por la Ayuda a los Grupos de Investigación (GR10120) del Gobierno de Extremadura (Consejería de Empleo, Empresa e Innovación); con la aportación de la Unión Europea a través de los Fondos Europeos de Desarrollo Regiona

Hydroelectric power plant management relying on neural networks and expert system integration

The use of Neural Networks (NN) is a novel approach that can help in taking decisions when integrated in a more general system, in particular with expert systems. In this paper, an architecture for the management of hydroelectric power plants is introduced. This relies on monitoring a large number of signals, representing the technical parameters of the real plant. The general architecture is composed of an Expert System and two NN modules: Acoustic Prediction (NNAP) and Predictive Maintenance (NNPM). The NNAP is based on Kohonen Learning Vector Quantization (LVQ) Networks in order to distinguish the sounds emitted by electricity-generating machine groups. The NNPM uses an ART-MAP to identify different situations from the plant state variables, in order to prevent future malfunctions. In addition, a special process to generate a complete training set has been designed for the ART-MAP module. This process has been developed to deal with the absence of data about abnormal plant situations, and is based on neural nets trained with the backpropagation algorithm.Publicad

Derivative observations in Gaussian Process models of dynamic systems

Gaussian processes provide an approach to nonparametric modelling which allows a straightforward combination of function and derivative observations in an empirical model. This is of particular importance in identification of nonlinear dynamic systems from experimental data. 1)It allows us to combine derivative information, and associated uncertainty with normal function observations into the learning and inference process. This derivative information can be in the form of priors specified by an expert or identified from perturbation data close to equilibrium. 2) It allows a seamless fusion of multiple local linear models in a consistent manner, inferring consistent models and ensuring that integrability constraints are met. 3) It improves dramatically the computational efficiency of Gaussian process models for dynamic system identification, by summarising large quantities of near-equilibrium data by a handful of linearisations, reducing the training size - traditionally a problem for Gaussian process models

Hybrid expert system of rough set and neural network

The combination of neural network and expert system can accelerate the process of inference, and then rapidly produce associated facts and consequences. However, neural network still has some problems such as providing explanation facilities, managing the architecture of network and accelerating the training time. Thus to address these issues we develop a new method for pre-processing based on rough set and merge it with neural network and expert system. The resulting system is a hybrid expert system model called a Hybrid Rough Neural Expert System (HRNES)

Bag-Level Aggregation for Multiple Instance Active Learning in Instance Classification Problems

A growing number of applications, e.g. video surveillance and medical image analysis, require training recognition systems from large amounts of weakly annotated data while some targeted interactions with a domain expert are allowed to improve the training process. In such cases, active learning (AL) can reduce labeling costs for training a classifier by querying the expert to provide the labels of most informative instances. This paper focuses on AL methods for instance classification problems in multiple instance learning (MIL), where data is arranged into sets, called bags, that are weakly labeled. Most AL methods focus on single instance learning problems. These methods are not suitable for MIL problems because they cannot account for the bag structure of data. In this paper, new methods for bag-level aggregation of instance informativeness are proposed for multiple instance active learning (MIAL). The \textit{aggregated informativeness} method identifies the most informative instances based on classifier uncertainty, and queries bags incorporating the most information. The other proposed method, called \textit{cluster-based aggregative sampling}, clusters data hierarchically in the instance space. The informativeness of instances is assessed by considering bag labels, inferred instance labels, and the proportion of labels that remain to be discovered in clusters. Both proposed methods significantly outperform reference methods in extensive experiments using benchmark data from several application domains. Results indicate that using an appropriate strategy to address MIAL problems yields a significant reduction in the number of queries needed to achieve the same level of performance as single instance AL methods