Approximate syllogistic reasoning: a contribution to inference patterns and use cases

In this thesis two models of syllogistic reasoning for dealing with arguments that involve fuzzy quantified statements and approximate chaining are proposed. The modeling of quantified statements is based on the Theory of Generalized Quantifiers, which allows us to manage different kind of quantifiers simultaneously, and the inference process is interpreted in terms of a mathematical optimization problem, which allows us to deal with more arguments that standard deductive ones. For the case of approximate chaining, we propose to use synonymy, as used in a thesaurus, for calculating the degree of confidence of the argument according to the degree of similarity between chaining terms. As use cases, different types of Bayesian reasoning (Generalized Bayes' Theorem, Bayesian networks and probabilistic reasoning in legal argumentation) are analysed for being expressed through syllogisms

Commonsense knowledge representation and reasoning with fuzzy neural networks

This paper highlights the theory of common-sense knowledge in terms of representation and reasoning. A connectionist model is proposed for common-sense knowledge representation and reasoning. A generic fuzzy neuron is employed as a basic element for the connectionist model. The representation and reasoning ability of the model is described through examples

A comparison of techniques for learning and using mathematics and a study of their relationship to logical principles

Various techniques exist for learning mathematical concepts, like experimentation and exploration, respectively using mathematics, like modelling and simulation. For a clear application of such techniques in mathematics education, there should be a clear distinction between these techniques. A recently developed theory of fuzzy concepts can be applied to analyse the four mentioned concepts. For all four techniques one can pose the question of their relationship to deduction, induction and abduction as logical principles. An empirical study was conducted with 12-13 aged students, aiming at checking the three reasoning processes

DEVELOPMENT OF A PROTOTYPE OF MALARIA CLINICAL DIAGNOSTIC DECISION SUPPORT SYSTEM

Introduction : Malaria is a public health problem that still causes mortality, particularly in high risk population. Kabupaten Nias is one of the malaria endemic areas. Malaria diagnosis is mainly determined according to physical examination, despite the fact that laboratory examination is the gold standard of malaria diagnosis. To help health workers in diagnosing malaria accurately, it is necessary to develop a decision support system for malaria diagnosis.Objectives: To develop a prototype of malaria diagnostic decision support system.Methods: It was a descriptive study with action research design to explore each phase in the development of a prototype of malaria diagnostic decision support system. Participants of the study consisted of 5 general practitioners in RSU Gunungsitoli and 2 nurses in Puskesmas Gunungsitoli.Results: The study created an application model of computer-based malaria diagnostic decision support system designed using PHP programming language and MySQL database. This system worked by entering malaria clinical symptoms into the expert system, and data of symptoms were processed by the expert system to determine diagnosis and medical advice that was useful to assist health staff in making decision.Conclusion: Malaria diagnostic decision support system that had been developed attracted the interest of health workers and help them in diagnosing malaria clinically.Keywords: diagnostic decision support system, prototype, malaria, computer application, Gunungsitol