An intelligent framework for monitoring student performance using fuzzy rule-based linguistic summarisation

Monitoring students' activity and performance is vital to enable educators to provide effective teaching and learning in order to better engage students with the subject and improve their understanding of the material being taught. We describe the use of a fuzzy Linguistic Summarisation (LS) technique for extracting linguistically interpretable scaled fuzzy weighted rules from student data describing prominent relationships between activity / engagement characteristics and achieved performance. We propose an intelligent framework for monitoring individual or group performance during activity and problem based learning tasks. The system can be used to more effectively evaluate new teaching approaches and methodologies, identify weaknesses and provide more personalised feedback on learner's progress. We present a case study and initial experiments in which we apply the fuzzy LS technique for analysing the effectiveness of using a Group Performance Model (GPM) to deploy Activity Led Learning (ALL) in a Master-level module. Results show that the fuzzy weighted rules can identify useful relationships between student engagement and performance providing a mechanism allowing educators to transparently evaluate teaching and factors effecting student performance, which can be incorporated as part of an automated intelligent analysis and feedback system

Team situation awareness measure using semantic utility functions for supporting dynamic decision-making

Team decision-making is a remarkable feature in a complex dynamic decision environment, which can be supported by team situation awareness. In this paper, a team situation awareness measure (TSAM) method using a semantic utility function is proposed. The semantic utility function is used to clarify the semantics of qualitative information expressed in linguistic terms. The individual and team situation awareness are treated as linguistic possibility distributions on the potential decisions in a dynamic decision environment. In the TSAM method, team situation awareness is generated through reasoning and aggregating individual situation awareness based on a multi-level hierarchy mental model of the team. Individual and team mental models are composed of key drivers and significant variables. An illustrative example in telecoms customer churn prediction is given to explain the effectiveness and the main steps of the TSAM method. © 2009 Springer-Verlag

Some views on information fusion and logic based approaches in decision making under uncertainty

Decision making under uncertainty is a key issue in information fusion and logic based reasoning approaches. The aim of this paper is to show noteworthy theoretical and applicational issues in the area of decision making under uncertainty that have been already done and raise new open research related to these topics pointing out promising and challenging research gaps that should be addressed in the coming future in order to improve the resolution of decision making problems under uncertainty

Linguistic quantifiers modeled by Sugeno integrals

Since quantifiers have the ability of summarizing the properties of a class of objects without enumerating them, linguistic quantification is a very important topic in the field of high level knowledge representation and reasoning. This paper introduces a new framework for modeling quantifiers in natural languages in which each linguistic quantifier is represented by a family of fuzzy measures, and the truth value of a quantified proposition is evaluated by using Sugeno's integral. This framework allows us to have some elegant logical properties of linguistic quantifiers. We compare carefully our new model of quantification and other approaches to linguistic quantifiers. A set of criteria for linguistic quantification was proposed in the previous literature. The relationship between these criteria and the results obtained in the present paper is clarified. Some simple applications of the Sugeno's integral semantics of quantifiers are presented. © 2006 Elsevier B.V. All rights reserved

Aplicação da lógica fuzzy em processos de decisão econômica

In the nonconventional economic literature decision processes are mainly analyzed on the basis of cognitive aspects (such as the existence of limited rationality) and institutional aspects (such as rules of thumb, institutions and conventions). The fuzzy logic, in turn, offers a form of treating the decision process when agents only have imprecise and subjective information in a context of complexity and uncertainty. This paper discusses the points of convergence and complementarities between the fuzzy logic and the theory of behavior based on limited rationality and rule-guided economic behavior.complexity; uncertainty; decision; fuzzy

FORT: una herramienta de regresión borrosa

El uso de las técnicas de regresión sobre las observaciones experimentales ha permitido el estudio de numerosos fenómenos en diversos campos de la ciencia, y muy especialmente en las ciencias sociales. Dichas técnicas requieren de un número suficiente de observaciones “precisas”, exactas y fiables. Sin embargo, no siempre es posible obtener el conjunto de observaciones necesario, o éstas contienen algún tipo de imperfección en los datos, debido a la imprecisión o vaguedad de los mismos. En cualquier caso, con suficientes datos o no, con imperfecciones o no, los modelos obtenidos deberían proveer de capacidades predictivas y descriptivas [JCr02]. Las actuales herramientas, o las más fácilmente accesibles, tienen limitado el uso de modelos y difícilmente usan las técnicas de la teoría de conjuntos borrosos. Se propone en este trabajo una herramienta abierta de regresión que admita el uso de cualquier modelo de curva independientemente de su naturaleza. Además, esta herramienta permitirá el uso de diferentes formas de borrosidad y por su diseño permitiría cualquier modelo propuesto por el usuario si éste prevee que éstos tienen características que sean suficientemente predictivas y descriptivas. Esta primera aproximación de una herramienta abierta de regresión se realiza un estudio sobre diferentes modelos paramétricos simbólicos, usados comúnmente en la práctica en disciplinas tan heterogéneas como pueden ser la Ingeniería del Software, la Economía o en cualquier campo en donde puedan aparecer imprecisiones en la información. [ABSTRACT] The use of regression techniques in experimental observations has led to the study of numerous phenomena in various fields of science, especially in social science. These techniques require a sufficient number of “precise”, exact and reliable observations. However, it is not always possible to obtain all the necessary group of observations or these have some failings, as a result of inexact or vague data. Nevertheless, having more or less data, with or without failings, the obtained paradigms should provide predictive and descriptive capacities. The current tools or those more accessible have limited paradigm application and hardly use the techniques relating the fuzzy sets theory. In this first approach to an open regression tool, a study has been carried out of the different parametric, symbolic paradigms, commonly used in the practice of such diverse disciplines as Software Engineering, Economy or any other field where information imprecision can appear

Artificial Intelligence in Engineering Management

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Predictive long-term asset maintenance strategy: development of a fuzzy logic condition-based control system

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceTechnology has accelerated the growth of the Facility Management industry and its roles are broadening to encompass more responsibilities and skill sets. FM budgets and teams are becoming larger and more impactful as new technological trends are incorporated into data-driven strategies. This new scenario has motivated institutions such as the European Central Bank to initiate projects aimed at optimising the use of data to improve the monitoring, control and preservation of the assets that enable the continuity of the Bank's activities. Such projects make it possible to reduce costs, plan, manage and allocate resources, reinforce the control, and efficiency of safety and operational systems. To support the long-term maintenance strategy being developed by the Technical Facility Management section of the ECB, this thesis proposes a model to calculate the Left wear margin of the equipment. This is accomplished through the development of an algorithm based on a fuzzy logic system that uses Python language and presents the system's structure, its reliability, feasibility, potential, and limitations. For Facility Management, this project constitutes a cornerstone of the ongoing digital transformation program