Thesis (Ph. D.)--College of Computing, Georgia Institute of Technology, 2005. Directed by Yannis Smaragdakis. / Spencer Rugaber, Committee Member ; Olin Shivers, Committee Member ; Mary Jean Harrold, Committee Member ; Yannis Smaragdakis, Committee Chair ; Philip Wadler, Committee Member. Includes bibliographical references.

Thesis (Ph.D.) - Carleton University, 2007. / Includes bibliographical references (p. 220-226). Also available in electronic format on the Internet.

Thesis (Ph. D.)--Albert-Ludwigs-Universität Freiburg im Breisgau, 2006. / Includes bibliographical references (p. 201-221) and index.

Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.

Η παρούσα εργασία έχει ως στόχο να παρουσιάσει έναν παράλληλο αλγόριθμο Εξόρυξης δεδομένων που μπορεί να εφαρμοστεί σε μεγάλες βάσεις δεδομένων με χρήση Επαγωγικού Λογικού Προγραμματισμού. Η κεντρική υπόθεση αυτής της εργασίας είναι πως είναι απαραίτητο αλλά και δυνατό να υιοθετηθούν παράλληλοι αλγόριθμοι στη διαδικασία της Εξόρυξης δεδομένων. Αποδεικνύεται ότι ο παραλληλισμός μπορεί να εφαρμοστεί αποδοτικά στον Επαγωγικό Λογικό Προγραμματισμό (Inductive Logic Programming - ILP). Η ολοκληρωμένη αναπαράσταση της γνώσης και η εξαιρετική ενσωμάτωση της υποκείμενης γνώσης του ILP αποδεικνύεται πολύ σημαντική μεταξύ των αλγορίθμων Εξόρυξης δεδομένων. Το Κεφάλαιο 1 αποτελεί μια εισαγωγή στην εξόρυξη δεδομένων. Παρουσιάζονται οι βασικές εργασίες που λαμβάνουν χώρα κατά την εξόρυξη δεδομένων καθώς και μερικοί από τους συνηθισμένους αλγορίθμους εξόρυξης δεδομένων. Στη συνέχεια, γίνεται μια εισαγωγή στη σχεσιακή εξόρυξη δεδομένων και δίνονται τα βασικά χαρακτηριστικά αλγορίθμων σχεσιακής εξόρυξης δεδομένων. Το Κεφάλαιο 2 αυτής της διπλωματικής εργασίας παρουσιάζει τη θεωρία του Eπαγωγικού Λογικού Προγραμματισμού (ΕΠΛ - ILP), μερικές δημοφιλείς μεθόδους ILP καθώς και τον τρόπο με τον οποίο εφαρμόζεται ο ILP για να επιλύσει προβλήματα που συναντώνται σε συστήματα βάσεων δεδομένων Παρουσιάζεται αναλυτικά μια συγκεκριμένη προσέγγιση στον ILP – η Mode Directed Inverse Entailment [60], καθώς αποτελεί τη βάση για την παράλληλη έκδοση της Progol που θα παρουσιαστεί σε επόμενο κεφάλαιο. Το Κεφάλαιο 3 είναι αφιερωμένο στην παρουσίαση διαφόρων συστημάτων ILP και την εφαρμογή τους στην Εξόρυξη δεδομένων. Στη συνέχεια του κεφαλαίου παρουσιάζεται ένας σειριακός αλγόριθμος ILP Εξόρυξης δεδομένων και η ανάλυση κόστους του. Γίνεται μια εισαγωγή στο Bulk Synchronous Parallelism (BSP) [46]. Το Κεφάλαιο 4, είναι μια σύνοψη της παράλληλης Επαγωγικής Λογικής. Στην αρχή αναφέρονται οι βασικές αρχές του παράλληλου επαγωγικού μοντέλου και στη συνέχεια ακολουθεί μια λεπτομερής περιγραφή. Εμφανίζονται τα προβλήματα μιας τέτοιας προσέγγισης και παρουσιάζεται μια ανάλυση κόστους. Τμήμα του Κεφαλαίου 4 είναι αφιερωμένο σε έναν παράλληλο αλγόριθμο ILP που χρησιμοποιεί το μοντέλο BSP. Για να υποστηριχθεί ο παράλληλος αλγόριθμος του Κεφαλαίου 4, το Κεφάλαιο 5 παρουσιάζει μερικά βασικά χαρακτηριστικά ενός παράλληλου συστήματος ILP – της παράλληλης Progol. Το σύστημα αυτό δημιουργήθηκε με βάση το μοντέλο BSP και βασίζεται στην Progol που υλοποιήθηκε (σε γλώσσα C) από το Muggleton. Παρέχονται πολλά παραδείγματα και επεξηγείται το φαινόμενο της υπεργραμμικής επιτάχυνσης (super-linear speedup). Στο Κεφάλαιο 6 έχουν συμπεριληφθεί τα αποτελέσματα των δοκιμών του αλγορίθμου του Κεφαλαίου 4 σε τρεις διαφορετικές περιπτώσεις προβλημάτων, στην ταξινόμηση των ζώων, στην εκμάθηση των κανόνων του σκάκι και στο πρόβλημα τερματισμού σε μια παρτίδα σκάκι. Το Κεφάλαιο 7 συνοψίζει τα αποτελέσματα της μεταπτυχιακής εργασίας. / -

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This project is part of the Graduate Program in Educational Technology, Development of Educational Technologies and it aims at developing, as a final product, a Serious Game named Super Mario Logic in order to help in the teaching and learning process of logic programming which is essential to the professional future of the area. It is through logic programming that students learn to develop algorithms, which are sequences of steps used to reach a specific result. From the development of algorithms there are the programming stages, in which a significant study of logic programming and algorithms plays a key role for the programmer. This project aims at developing a serious game that challenges students into solving problems in logic programming. Serious games are becoming popular as they help students learn, making them interact and learn in an educational and playful way beyond the traditional teaching pattern. The game is being developed through a methodology called INTERAD, which is used for the development of Educational Interfaces. / Esta dissertação faz parte do Programa de Pós-Graduação Stricto Sensu em Tecnologias Educacionais em Rede, linha de Desenvolvimento de Tecnologias Educacionais em Rede e tem como produto final um Jogo Sério chamado Super Mário Logic, para Lógica de Programação, disciplina esta essencial ao futuro profissional da área. Por meio dela, o estudante aprende a desenvolver algoritmos, que são sequências de passos para chegar a um determinado resultado. A partir do desenvolvimento de algoritmos tem-se a etapa da programação, na qual um significante estudo da Lógica de Programação e de Algoritmos desempenham papel fundamental para o programador. Este trabalho foi elaborado com o objetivo de desenvolver um Jogo Sério para desafiar o aluno a resolver problemas de lógica de programação. Os Jogos Sérios estão se popularizando, pois ajudam na aprendizagem do aluno, envolvendo-o, fazendo-o interagir e gerar resultados de uma forma educativa e lúdica, saindo do padrão de ensino tradicional. Os Jogos Sérios têm por finalidade melhorar aspectos específicos da aprendizagem, com o intuito de reforçar um aprendizado específico. O jogo foi desenvolvido por meio da metodologia INTERAD, utilizada para desenvolvimento de Interfaces Educacionais.

abstract: Different logic-based knowledge representation formalisms have different limitations either with respect to expressivity or with respect to computational efficiency. First-order logic, which is the basis of Description Logics (DLs), is not suitable for defeasible reasoning due to its monotonic nature. The nonmonotonic formalisms that extend first-order logic, such as circumscription and default logic, are expressive but lack efficient implementations. The nonmonotonic formalisms that are based on the declarative logic programming approach, such as Answer Set Programming (ASP), have efficient implementations but are not expressive enough for representing and reasoning with open domains. This dissertation uses the first-order stable model semantics, which extends both first-order logic and ASP, to relate circumscription to ASP, and to integrate DLs and ASP, thereby partially overcoming the limitations of the formalisms. By exploiting the relationship between circumscription and ASP, well-known action formalisms, such as the situation calculus, the event calculus, and Temporal Action Logics, are reformulated in ASP. The advantages of these reformulations are shown with respect to the generality of the reasoning tasks that can be handled and with respect to the computational efficiency. The integration of DLs and ASP presented in this dissertation provides a framework for integrating rules and ontologies for the semantic web. This framework enables us to perform nonmonotonic reasoning with DL knowledge bases. Observing the need to integrate action theories and ontologies, the above results are used to reformulate the problem of integrating action theories and ontologies as a problem of integrating rules and ontologies, thus enabling us to use the computational tools developed in the context of the latter for the former. / Dissertation/Thesis / Ph.D. Computer Science 2012

Nous nous intéressons aux approches formelles pour le développement de systèmes réactifs critiques. Le langage synchrone Lustre pour la spécification de tels systèmes a subit des évolutions majeurs au cours des dernières années en intégrant dans sa sémantique à base flots de données synchrones des constructions de plus haut-niveau appelées automates de modes (dans le langage Scade 6). Ceux-ci mettent en œuvre l’activation de modes de calculs en fonction des états et des transitions de l’automate, et reposent pour cela sur la sémantique des horloges du langage Lustre. En particulier, nous étudiions la prise en compte des horloges et des automates de modes dans l’outil de génération de tests GATeL dédié à l’origine au langage Lustre mono-horloge (flots de données purs). GATeL génère automatiquement des séquences de tests pour un modèle à partir d’un objectif de test décrit en Lustre à travers une exploration en arrière des dépendances entre flots et selon des teniques de résolution de contraintes. Nous présentons ces différents domaines et la mise en oeuvre des modifications apportées à l’outil pour prendre en compte les automates de modes. Enfin, nous définissons des critères de couverture structurelle pour les automates de modes et montrons alors comment, en les traduisant de manière automatique sous forme d’objectifs de tests, GATeL permet de générer des séquences couvrant ces critères. / Lustre is a synchronous dataflow-oriented language for the specification of reactive systems. Since its definition, it has been extended to support mode automata, a formalism in which computation modes are activated according to an extended state-machine. The semantics of mode-automata is heavily based on an appropriate use of the clock sampling features of Lustre. We present the modifications made in GATeL, an automatic test sequences generator originally designed for a mono-rate subset of Lustre. GATeL performs a lazy goal-oriented test sequences generation, based on constraint logic programming. We modify it so that it can handle the temporal constraints of clocks internally and efficiently generate tests sequences from state-maines specifications. We also present some existing structural test criteria for state-machines and adapt them to the specific case of mode-automata.

Este trabalho investiga Aprendizado Indutivo de Máquina como função das linguagens de descrição, utilizadas para expressar instancias, conceitos e teoria do domínio. A ampliação do poder de representação do aprendizado proporcional e abordada no contexto de indução construtiva, no domínio de funções booleanas, com a proposta de uma estratégia de composição de atributos denominada root-fringe. Avaliações experimentais dessa e de outras estratégias de construção de novos atributos foram conduzidas e os resultados analisados. Dois métodos de poda, para tratamento de ruídos, em aprendizado de arvores de decisão, foram avaliados num ambiente de indução construtiva e os resultados discutidos. Devido a limitação do aprendizado proposicional, foram investigadas formas de ampliação dos limites do aprendizado, através da ampliação do poder representacional das linguagens de descrição. Foi escolhida Programação Lógica Indutiva - PLI - que e um paradigma de aprendizado indutivo que usa restrições de Lógica de Primeira Ordem como linguagens de descrição. O aprendizado em PLI só é factível quando as linguagens utilizadas estão restritas e é fortemente controlado, caso contrário, o aprendizado em PLI se torna indecidível. A pesquisa em PLI se direcionou a formas de restrição das linguagens de descrição da teoria do domínio e de hipóteses. Três algoritmos que \"traduzem\" a teoria do domínio de sua forma intencional, para extensional, são apresentados. As implementações de dois deles são discutidas. As implementações realizadas deram origem a dois ambientes experimentais de aprendizado: o ambiente proposicional experimental, do qual fazem parte o ambiente experimental construtivo, e o ambiente experimental relacional. / This work investigates Inductive Machine Learning as a function of the description languages employed to express instances, concepts and domain theory. The enlargement of the representational power of propositional learning methods is approached via constructive induction, in the domain of boolean functions, through the proposal of a bias for composing attributes, namely, the bias root-fringe. Experimental evaluation of root-fringe, as well as other biases for constructing new attributes was conducted and the results analyzed. Two pruning methods for decision trees were evaluated in an environment of constructive induction and the results discussed. Due to the limitations of propositional learning, ways of enlarging the limits of the learning process were investigated through enlarging the representational power of the description languages. It was chosen Inductive Logic Programming - ILP - that is an inductive learning paradigm that uses restrictions of First Order Logic as description languages. Learning using ILP is only feasible when the languages are restricted and are strongly controlled; otherwise, learning in ILP becomes undecidible. Research work in ILP was directed towards restricting domain theory and hypotheses description languages. Three algorithms that \"translate\" the intentional expression of a domain theory into its extensional expression are presented. The implementations of two of them are discussed. The implementations gave rise to two experimental learning environments: the propositional environment, which includes the constructive environment, and the relational environment.