Σε αυτήν την εργασία μελετάται ο Επαγωγικός Λογικός Προγραμματισμός μέσα απο το πρίσμα της μάθησης της Γραμματικής της αγγλικής γλώσσας. Ο λόγος που επιλέχθηκε αυτό το πρόβλημα είναι ότι εξομοιώνει, μέχρι ενός σγημείου, τον τρόπο που τα παιδιά μαθαίνουν να μιλούν κάποια γλώσσα, υπό την έννοια ότι μαθαίνουν να μιλάνε χωρίς να έρθουν σε επαφή με τους κανόνες – τη γραμματική – της γλώσσας, αλλά από την επαφή τους με τα με τα ερεθίσματα – τα παραδείγματα – που έχουν από τον περίγυρό τους. / This paper is about Inductive Logic Programming through the prism of a problem. In our case Grammar Learning.

Made available in DSpace on 2014-06-12T15:52:36Z (GMT). No. of bitstreams: 1 license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2009 / Devido á grande demanda por softwares cada vez mais robustos, complexos e flexíveis, e, sobretudo, pelo curtíssimo tempo de entrega exigido, a engenharia de software tem procurado novos meios de desenvolvimento que supram satisfatoriamente essas demandas. Uma forma de galgar esses novos patamares de produtividade provém do uso de uma metodologia baseada em agentes que se comunicam e com isso, ao invés dos programas serem estritamente programados, o comportamento destes sistemas de software emerge da interação de agentes, robôs, ou subsistemas aut onomos, independentes, além de declarativamente especificados. Isto provê a habilidade para automaticamente configurá -los, otimizá-los, monitorá-los, adaptá-los, diagnosticá-los, repará-los e protegê-los dentro do ambiente. Contudo, um grande problema das linguagens declarativas é a falta de mecanismos que permitem a melhor estruturação de dados, facilitando portanto, o reuso. Portanto, esta dissertação explica o desenvolvimento de nova linguagem lógica declarativa para programar sistemas de raciocínio automático de uma forma modularizada: C2HR∨. A linguagem base escolhida para a extensão com componentes lógicos foi CHR. Os motivos para essa escolha são definidos ao longo da dissertação. Duas abordagens, portanto, são apresentadas: a primeira, conhecida como CHRat, foi desenvolvida numa parceria juntamente com o grupo de pesquisas CONTRAINTES do INRIA/Rocquencourt-Paris, onde o programador ´e o responsável direto por definir os componentes CHR, permitindo o seu reuso por outros componentes; a segunda aplicação, CHRtp, visa atender prioritariamente requisitos de completude e, por isso, se baseia em procedimentos lógicos de inferência como: o raciocínio para frente, o raciocínio para trás, e a resolução/factoring. A dissertação mostra também alguns exemplos práticos, onde uso de componentes facilita radicalmente sua implementação. As contribuições almejadas com essa dissertação são: a definição de uma família bem formalizada de provadores de teoremas automáticos, que podem trabalhar com sentenças especificadas em lógica horn ou em lógica de primeira ordem, a extensão de CHR como uma linguagem modular de propósito geral, a melhor estruturação de bases conhecimentos e até o uso em conjunto de bases heterogêneas, a definição de uma linguagem para a fácil e direta estruturação de dados por meio de componentes, dentre outras

[pt] Haeusler e Pequeno desenvolveram um método de prova de teoremas baseado em jogos semânticos introduzidos por Hintikka. Posteriormente Rios desenvolveu o programa PROGRAME que implementa este método. Este trabalho descreve um ambiente de programação lógica desenvolvido com a finalidade de oferecer ao usuário facilidades de programação lógica suportada pelo provador PROGRAME. O ambiente foi idealizado o mais próximo possível nos ambientes PROLOG, com a facilidade adicional de permitir que no código do programa a ele submetido sejam utilizados indiferentemente goals a serem provados neste ambiente ou PROGRAME. / [en] Haeusler and Pequeno developed a theorem proving method based on semântica games introduced by Hintikka. Later, Rios developed the program PROGRAME witch implements this method. Our work describes a logic programming environment developed with the aim of offering the user logic programming facilities supported by the PROGRAME prover. The programming environment was chosen to be PROLOG-like with additional facility of permiting that a given goal can b proved both by or environment and by the PROGRAME prover

[pt] A gerência de redes telefônicas nas modernas concessionáras do chamado Primeiro Mundo têm se apoiado, cada vez mais, sobre sistemas baseados em conhecimento. Esses sistemas inteligentes são apresentados como solução capaz de fazer face às exigências de qualidade e confiabilidade feitas pelos usuários ligados a redes, as quais crescem em dimensão e complexidade, num ritmo jamais visto. Este trabalho incursiona nesse campo e apresenta o projeto, seu desenvolvimento e a implementação, baseada na Programação em Lógica, de um sistema para auxílio à emissão de telecomandos sobre a Rede Nacional de Telefonia, operada pela EMBRATEL. Adicionalmente, apresentamos um levantamento das principais técnicas de obtenção e representação de conhecimento para fins de utilização em computadores eletrônicos. / [en] The management of telephone networks in developed countries hás been based, in a ever-growing scale, on knowledge-based systems. Those intelligent systems have been presented as a means to face the demands in quality and reliability posed by users of those networks, which have been expanding at an uncredibly fast pace. This paper tries to explore this area and presents the project, its development and implement based on Logic Programming, of a system for aiding the sending commands upon the National Telephone Network, operated by EMBRATEL. Additionally we have done a survey of the main techniques for the acquisition and representation of knowledge related to electronic computing

Efficient unification is considered within the context of logic programming. Unification is explained in terms of equivalence classes made up of terms, where there is a constraint that no equivalence class may contain more than one function term. It is demonstrated that several well-known "efficient" but nonlinear unification algorithms continually maintain the said constraint as a consequence of their choice of data structure for representing equivalence classes. The linearity of the Paterson-Wegman unification algorithm is shown largely to be a consequence of its use of unbounded lists of pointers for representing equivalences between terms, which allows it to avoid the nonlinearity of "union-find".

A new declarative language is presented which captures the expressibility of both logic programming languages and functional languages. This is achieved by conditional graph rewriting, with full unification as the parameter passing mechanism. The syntax and semantics are described both formally and informally, and examples are offered to support the expressibility claim made above. The language design is of further interest due to its uniformity and the inclusion of a novel mechanism for type inference in the presence of derived type hierarchies

In the past decade, fast advancements have been made in the sequencing, digitalization and collection of the biological data. However the bottleneck remains at the point of analysis and extraction of patterns from the data. We have developed a method that is aimed at widening this bottleneck by automating the knowledge extraction from the biological data. Our approach is aimed at discovering patterns in a set of DNA sequences based on the location of transcription factor binding sites or any other biological markers with the emphasis of discovering relationships. A variety of statistical and computational methods exists to analyze such data. However, they either require an initial hypothesis, which is later tested, or classify the data based on its attributes. Our approach does not require an initial hypothesis and the classification it produces is based on the relationships between attributes. The value of such approach is that is is able to uncover new knowledge about the data by inducing a general theory based on basic known rules. The core of our approach lies in an inductive logic programming engine, which, based on positive and negative examples as well as background knowledge, is able to induce a descriptive, human-readable theory, describing the data. An application provides an end-to-end analysis of DNA sequences. A simple to use Web interface accepts a set of related sequences to be analyzed, set of negative example sequences to contrast the main set (optional), and a set of possible genetic markers as position-specific scoring matrices. A Java-based backend formats the sequences, determines the location of the genetic markers inside them and passes the information to the ILP engine, which induces the theory. The model, assumed in our background knowledge, is a set of basic interactions between biological markers in any DNA sequence. This makes our approach applicable to analyze a wide variety of biological problems, including detection of cis-regulatory modules and analysis of ChIP-Sequencing experiments. We have evaluated our method in the context of such applications on two real world datasets as well as a number of specially designed synthetic datasets. The approach has shown to have merit even in situations when no significant classification could be determined.

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Ο Επαγωγικός Λογικός Προγραμματισμός (Inductive Logic Programming ή, σε συντομογραφία ILP) είναι ο ερευνητικός τομέας της Τεχνητής Νοημοσύνης (Artificial Intelligence) που δραστηριοποιείται στη τομή των γνωστικών περιοχών της Μάθησης Μηχανής (Machine Learning) και του Λογικού Προγραμματισμού (Logic Programming).Ο όρος επαγωγικός εκφράζει την ιδέα του συλλογισμού από το επί μέρους στο γενικό. Μέσω της επαγωγικής μάθησης μηχανής ο ILP επιτυγχάνει το στόχο του που είναι η δημιουργία εργαλείων και η ανάπτυξη τεχνικών για την εξαγωγή υποθέσεων από παρατηρήσεις (παραδείγματα) και η σύνθεση-απόκτηση νέας γνώσης από εμπειρικές παρατηρήσεις. Σε αντίθεση με της περισσότερες άλλες προσεγγίσεις της επαγωγικής μάθησης ο ILP ενδιαφέρεται για της ιδιότητες του συμπερασμού με κανόνες για την σύγκλιση αλγορίθμων και για την υπολογιστική πολυπλοκότητα των διαδικασιών. Ο ILP ασχολείται με την ανάπτυξη τεχνικών και εργαλείων για την σχεσιακή ανάλυση δεδομένων. Εφαρμόζεται απευθείας σε δεδομένα πολλαπλών συσχετισμών για την ανακάλυψη προτύπων. Τα πρότυπα που ανακαλύπτονται από τα συστήματα στον ILP εκφράζονται ως λογικά προγράμματα. Τα λογικά προγράμματα αποτελούνται από ειδικούς κανόνες, οι οποίοι χωρίζονται στις προϋποθέσεις και στα συμπεράσματα. Ο ILP έχει χρησιμοποιηθεί εκτεταμένα σε προβλήματα που αφορούν τη μοριακή βιολογία, την βιοχημεία και την χημεία. Τα παραδείγματα, οι κανόνες εκφράζουν την γνώση υποβάθρου εκφράζονται σε μια γλώσσα λογικού προγραμματισμού όπως η Prolog. Ο Επαγωγικός Λογικός Προγραμματισμός διαφοροποιείται από τις άλλες μορφές Μάθησης Μηχανής, αφ’ ενός μεν λόγω της χρήσης μιας εκφραστικής γλώσσας αναπαράστασης και αφ’ ετέρου από τη δυνατότητά του να χρησιμοποιεί τη γνώση υποβάθρου. Διάφορες εφαρμογές έχουν αναπτυχθεί, εκ των οποίων η πιο πρόσφατη είναι η Progol, που αποτελείται από ένα διερμηνέα της Prolog ο οποίος συνοδεύεται από έναν αλγόριθμο Αντίστροφης Συνεπαγωγής (Inverse Entailment) που κατασκευάζει νέες προτάσεις με τη γενίκευση των παραδειγμάτων που περιέχονται στη βάση δεδομένων της Prolog. Η θεωρία του Επαγωγικού Λογικού Προγραμματισμού εγγυάται ότι η Progol θα διεξάγει μια αποδεκτή αναζήτηση στο διάστημα των γενικεύσεων, βρίσκοντας το ελάχιστο σύνολο προτάσεων, από το οποίο όλα τα παραδείγματα μπορούν να προκύψουν. Στην συγκεκριμένη εργασία η Progol είναι το εργαλείο που χρησιμοποιείται για την ανάπτυξη των παραδειγμάτων εφαρμογής του ILP. / The Inductive Reasonable Planning (Inductive Logic Programming or, in abbreviation ILP) is the inquiring sector Artificial Intelligence that is activated in the section of cognitive regions of Learning of Machine (Machine Learning) and Reasonable Planning (Logic Programming). The term inductive expresses the idea of reasoning from on part in general. Via the inductive learning of machine the ILP achieves his objective that is the creation of tools and the growth of techniques for the export of affairs from observations (examples) and composition of new knowledge from empiric observations. Contrary to more other approaches of inductive learning the ILP is interested for its inference attributes with rules on the convergence of algorithms and on the calculating complexity of processes. The ILP deals with the growth of techniques and tools for the relational analysis of data. It is applied directly in data of multiple correlations on the discovery of models. The models that are discovered by the systems in the ILP are expressed as reasonable programs. The reasonable programs are constituted by special rules, which are separated in the conditions and in the conclusions. The ILP has been used extensive in problems that concern the molecular biology, the biochemistry and the chemistry. The examples, the rules express the knowledge of background are expressed in a language of reasonable planning as the Prolog. The Inductive Reasonable Planning is differentiated by the other forms of Learning of Machine, on the one hand men because the use of expressive language of representation and on the other hand by his possibility of using the knowledge of background. Various applications have been developed, from which most recent is Progol, that is constituted from interpreter of Prolog which is accompanied by a algorithm of Inverse Entailment that manufactures new proposals with the generalisation of examples that is contained in the base of data of Prolog. theory of Inductive Reasonable Planning guarantees that the Progol will carry out a acceptable search in the interval of generalisations, finding the minimal total of proposals, from which all the examples can result. In the particular work the Progol is the tool that is used for the growth of examples of application of ILP.