Orientador: Henrique Lazari / Banca: Adilson José Vieira Brandão / Banca: Wlademir Seixas / Resumo: Desde cedo entramos em contato com as implicações lógicas. O binômio verdadeiro-falso está sempre presente em nossas vidas e nós nos acostumamos a aceitar que as coisas ou são verdadeiras ou são falsas. Divertimo-nos quando alguém nos conta histórias interessantes envolvendo lógica e que terminam em contradições, tais como, por exemplo, a do barbeiro que pode e não pode barbear a si mesmo, ou como a do advogado que consegue ganhar ou perder a mesma causa. Apreciamos mais "paradoxos" sem nos apercebermos que por trás deles existe toda uma teoria matemática, a chamada lógica fuzzy. Essa dissertação tem por objetivo apresentar um resumo deste teoria, mostrando como ela trata a existência de tais paradoxos e dar detalhes sobre uma visão compacta dos conjuntos fuzzy, a saber, utilizando uma representação geométrica. A análise de alguns resultados sobre tais conjuntos usando esta representação leva a uma justificativa para o estudo da lógica fuzzy, a saber, a diferença entre "fuzziness" e probabilidade, incluindo uma demonstração de que "fuzziness", de fato, existe / Abstract: Early on we got in touch with the logical implications. The binomial true-false is always present in our lives and we have come to accept that things are either true or false. Have fun when somebody tells interesting stories involving logic and ending with contradictions, such as, for example, the barber who can and can not shave himself, or as the lawyer who can win or lose the same cause. Appreciate more "paradoxes" without realizing that behind them there is a whole mathematical theory, called fuzzy logic. This thesis aims to present a summary of this theory, showing how it treats the existence of such paradoxes and give details about a compact view of fuzzy sets, namely, using a geometrical representation. The analysis of some results on such sets using this representation leads to a justification for the study of fuzzy logic, namely the difference between "fuzziness" and probability, including a demonstration that "fuzziness" in fact, exists / Mestre

Made available in DSpace on 2014-06-11T19:24:55Z (GMT). No. of bitstreams: 0 Previous issue date: 2010-10-14Bitstream added on 2014-06-13T18:52:59Z : No. of bitstreams: 1 dias_chbb_me_rcla.pdf: 692647 bytes, checksum: 869fdb9734d900054c9edcfce9ca37cc (MD5) / Universidade Estadual Paulista (UNESP) / Desde cedo entramos em contato com as implicações lógicas. O binômio verdadeiro-falso está sempre presente em nossas vidas e nós nos acostumamos a aceitar que as coisas ou são verdadeiras ou são falsas. Divertimo-nos quando alguém nos conta histórias interessantes envolvendo lógica e que terminam em contradições, tais como, por exemplo, a do barbeiro que pode e não pode barbear a si mesmo, ou como a do advogado que consegue ganhar ou perder a mesma causa. Apreciamos mais paradoxos sem nos apercebermos que por trás deles existe toda uma teoria matemática, a chamada lógica fuzzy. Essa dissertação tem por objetivo apresentar um resumo deste teoria, mostrando como ela trata a existência de tais paradoxos e dar detalhes sobre uma visão compacta dos conjuntos fuzzy, a saber, utilizando uma representação geométrica. A análise de alguns resultados sobre tais conjuntos usando esta representação leva a uma justificativa para o estudo da lógica fuzzy, a saber, a diferença entre fuzziness e probabilidade, incluindo uma demonstração de que fuzziness, de fato, existe / Early on we got in touch with the logical implications. The binomial true-false is always present in our lives and we have come to accept that things are either true or false. Have fun when somebody tells interesting stories involving logic and ending with contradictions, such as, for example, the barber who can and can not shave himself, or as the lawyer who can win or lose the same cause. Appreciate more paradoxes without realizing that behind them there is a whole mathematical theory, called fuzzy logic. This thesis aims to present a summary of this theory, showing how it treats the existence of such paradoxes and give details about a compact view of fuzzy sets, namely, using a geometrical representation. The analysis of some results on such sets using this representation leads to a justification for the study of fuzzy logic, namely the difference between fuzziness and probability, including a demonstration that fuzziness in fact, exists

Στην εργασία που ακολουθεί, ασχολούμαστε με την εφαρμογή μεθόδων Τεχνητής Νοημοσύνης σε ένα πραγματικό πρόβλημα, που αναφέρεται στην διάγνωση του βαθμού ασφαλισιμότητας ενός πελάτη μιας ασφαλιστικής εταιρείας. Η ανάγκη για την εφαρμογή αυτή προέκυψε από το γεγονός ότι πολλές φορές ο εμπειρογνώμονας της εταιρείας δεν είναι διαθέσιμος, αλλά και όταν είναι, χρειάζεται ένα συμβουλευτικό πρόγραμμα. Πιο συγκεκριμένα, για τη λύση του προβλήματος χρησιμοποιούνται: α) ένα ασαφές έμπειρο σύστημα υλοποιημένο με τη βοήθεια του εργαλείου FuzzyCLIPS, β) ένα έμπειρο σύστημα που χρησιμοποιεί κανόνες με συντελεστές βεβαιότητας τύπου MYCIN, γ) ένα έμπειρο σύστημα που χρησιμοποιεί κανόνες με συντελεστές βεβαιότητας τύπου weighted, υλοποιημένα και τα δύο με βάση το εργαλείο CLIPS και δ) ένα νευρωνικό δίκτυο υλοποιημένο με βάση το εργαλείο WEKA. Στο τέλος συγκρίνουμε τα παραπάνω συστήματα με βάση κάποιες μετρικές. Πριν να ξεκινήσουμε την ανάλυση του προβλήματός μας και των υλοποιήσεων των παραπάνω συστημάτων, αναλύουμε λίγο παραπάνω τους όρους και τα εργαλεία που ήδη αναφέραμε, δίνοντας περισσότερες πληροφορίες για την προέλευση τους, τα χαρακτηριστικά τους, τη χρησιμότητά τους, κτλ. Έτσι, αρχικά δίνουμε περισσότερα στοιχεία για τον τομέα της Τεχνητής Νοημοσύνης και πώς αυτός έχει εξελιχτεί στις τελευταίες δεκαετίες, και αναλύουμε τη συσχέτιση των Έμπειρων Συστημάτων με την Τεχνητή Νοημοσύνη, τα χαρακτηριστικά τους, τη δομή τους, τα πλεονεκτήματα και μειονεκτήματά τους. Στη συνέχεια, αναλύουμε τα τρία εργαλεία που θα χρησιμοποιήσουμε και τις δυνατότητες αυτών. Κι αφού δώσουμε περισσότερες πληροφορίες για το πρόβλημα της ‘Ασφαλισιμότητας’ και τον τρόπο που το αντιμετωπίζουμε, γίνεται παρουσίαση των παραπάνω ευφυών συστημάτων και των αποτελεσμάτων τους σε συγκεκριμένο σύνολο δεδομένων. Τέλος, προχωράμε σε σύγκριση και σχολιασμό των τιμών των μετρικών που προέκυψαν από τις προηγούμενες εφαρμογές, και εξαγωγή των συμπερασμάτων της σύγκρισης. / In the work that follows, we deal with the application of methods of Artificial Intelligence in a real problem, that is concerned with the diagnosis of degree of ‘how safe is to insure a customer’ in an insurance company. The need for this application resulted from the fact that many times over, the expert of the insurance company may not be available, but also when he is, he could use an advisory program. To be more exact, for the solution of the problem described above we use: a) a fuzzy expert system (in our case we use FuzzyCLIPS), b) an expert system that use rules with certainty factors as in the MYCIN tool, c) an expert system that uses rules with certainty factors as in the Weighted tool, both programmed using the CLIPS expert systems tool d) a neural network through WEKA neural network producer tool. Finally, we compare the above mentioned systems by calculating a set of metrics to conclude which method produces the most accurate results. Before analysing our problem and running the systems mentioned above, we analyze fatherly the terms and the tools that we use, providing more information on their characteristics, usefulness, etc. Thus, initially we give more information about Artificial Intelligence and how it has developed in the last decades, and we analyze the cross-correlation of Expert Systems with Artificial Intelligence, their characteristics, their structure, their advantages and disadvantages. After that, we analyze the three tools that we will use, and their possibilities, advantages and disadvantages. After giving more information on the problem of ‘how safe is to insure a customer’ and the way we deal with it, we present the above expert systems and their results in a specific dataset. Finally, we compare the metrics that were calculated from the previous applications, and comment on the conclusions of this comparison.