Temporal Action Logic is a well established logical formalism for reasoning about action and change that has long been used as a formal specification language. Its first-order characterization and explicit time representation makes it a suitable target for automated theorem proving and the application of temporal constraint solvers. We introduce a translation from a subset of Temporal Action Logic to constraint logic programs that takes advantage of these characteristics to make the logic applicable, not just as a formal specification language, but in solving practical reasoning problems. Extensions are introduced that enable the generation of action sequences, thus paving the road for interesting applications in deductive planning. The use of qualitative temporal constraints makes it possible to follow a least commitment strategy and construct partially ordered plans. Furthermore, the logical language and logic program translation is extended with the notion of composite actions that can be used to formulate and execute scripted plans with conditional actions, non-deterministic choices, and loops. The resulting planner and reasoner is integrated with a graphical user interface in our autonomous helicopter research system and applied to logistics problems. Solution plans are synthesized together with monitoring constraints that trigger the generation of recovery actions in cases of execution failures.

While most statistical learning methods are designed to work with data stored in a single table, many large datasets are stored in relational database systems. Probabilistic Relational Models (PRM) extend Bayesian networks by introducing relations and individuals, thus making it possible to represent information in a relational database. However, learning a PRM from relational data is a more complex task than learning a Bayesian Network from \"flat\" data. The main difficulties that arise while learning a PRM are establishing what are the legal dependency structures, searching for possible structures, and scoring them. This thesis focuses on the development of a novel approach to learn the structure of a PRM, describes a package in the R language to support the learning framework, and applies it to a real, large scale scenario of a city named Atibaia, in the state of São Paulo, Brazil. The research is based on a database combining three different tables, each representing one class in the domain of study. The first table contains 27 attributes from 110,816 citizens of Atibaia. The second table contains 9 attributes from 20,162 companies located in the city. And finally, the third table has 8 attributes from 327 census sectors (small territorial units that comprise the city of Atibaia). The proposed framework is applied to learn a PRM structure and parameters from the database. The model is used to verify if the Social Class of a person can be explained by the location where they live, their neighbors, and the companies nearby. Preliminary experiments have been conducted and a paper published in the 2017 Symposium on Knowledge Discovery, Mining and Learning (KDMiLe). The algorithm performance was further evaluated by extensive experimentation, and a broader study using Serasa Experian data was conducted. Finally, the package in the R language that supports our method was refined along with proper documentation and a tutorial. / Embora a maioria dos métodos de aprendizado estatístico tenha sido desenvolvida para se trabalhar com dados armazenados em uma única tabela, muitas bases de dados estão armazenadas em bancos de dados relacionais. Modelos Probabilísticos Relacionai (PRM) estendem Redes Bayesianas introduzindo relações e indivíduos, tornando possível a representação de informação em uma base de dados relacional. Entretanto, aprender um PRM através de dados relacionais é uma tarefa mais complexa que aprender uma Rede Bayesiana de uma única tabela. As maiores dificuldades que se impõe enquanto se aprende um PRM são estabelecer quais são as estruturas de dependência legais, procurar por possíveis estruturas, e avalia-las. Esta tese foca em desenvolver um novo método de aprendizado de estruturas de PRM, descrever um pacote na linguagem R que suporte este método e aplica-lo a um cenário real e de grande escala, a cidade de Atibaia, no estado de São Paulo, Brasil. Esta pesquisa está baseada em uma base de dados combinando três tabelas distintas, cada uma representando uma classe no domínio de estudo. A primeira tabela contém 27 atributos de 110.816 habitantes de Atibaia, e a segunda tabela contém 9 atributos de 20.162 empresas da cidade. Por fim, a terceira tabela possui 8 atributos para 327 setores censitários (pequenas unidades territoriais que formam a cidade de Atibaia). A proposta é aplicada para aprender-se a estrutura de um PRM e seus parâmetros através desta base de dados. O modelo foi utilizado para verificar se a classe social de uma pessoa pode ser explicada pelo local onde ela vive, seus vizinhos e as companhias próximas. Experimentos preliminares foram conduzidos e um artigo foi publicado no Symposium on Knowledge Discovery, Mining and Learning (KDMiLe). O desempenho do algoritmo foi reavaliada através de extensiva experimentação, e um estudo mais amplo foi conduzido com os dados da Serasa Experian. Por fim, o pacote em R que suporta o método proposto foi refinado, e documentação e tutorial apropriado foram descritos.

Harmony is the aspect of music concerned with the structure, progression, and relation of chords. In Western tonal music each period had different rules and practices of harmony. Similarly some composers and musicians are recognised for their characteristic harmonic patterns which differ from the chord sequences used by other musicians of the same period or genre. This thesis is concerned with the automatic induction of the harmony rules and patterns underlying a genre, a composer, or more generally a 'style'. Many of the existing approaches for music classification or pattern extraction make use of statistical methods which present several limitations. Typically they are black boxes, can not be fed with background knowledge, do not take into account the intricate temporal dimension of the musical data, and ignore rare but informative events. To overcome these limitations we adopt first-order logic representations of chord sequences and Inductive Logic Programming techniques to infer models of style. We introduce a fixed length representation of chord sequences similar to n-grams but based on first-order logic, and use it to characterise symbolic corpora of pop and jazz music. We extend our knowledge representation scheme using context-free definite-clause grammars, which support chord sequences of any length and allow to skip ornamental chords, and test it on genre classification problems, on both symbolic and audio data. Through these experiments we also compare various chord and harmony characteristics such as degree, root note, intervals between root notes, chord labels and assess their characterisation and classification accuracy, expressiveness, and computational cost. Moreover we extend a state- of-the-art genre classifier based on low-level audio features with such harmony-based models and prove that it can lead to statistically significant classification improvements. We show our logic-based modelling approach can not only compete with and improve on statistical approaches but also provides expressive, transparent and musicologically meaningful models of harmony which makes it suitable for knowledge discovery purposes.

The complexity of biological systems constitute a significant problem for the development of biological models. This inspired the creation of a few Computational Scientific Discovery systems that attempt to address this problem in the context of metabolomics through the use of computers and automation. These systems have important limitations, however, like limited revision and experiment design abilities and the inability to revise refuted models. The goal of this project was to address some of these limitations. The system developed for this project, "Huginn", was based on the use of Abductive Logic Programming to automate crucial development tasks, like experiment design, testing consistency of models with experimental results and revision of refuted models. The main questions of this project were (1) whether the proposed system can successfully develop Metabolic Network Models and (2) whether it can do it better than its predecessors. To answer these questions we tested Huginn in a simulated environment. Its task was to relearn the structures of disrupted fragments of a state-of-the-art model of yeast metabolism. The results of the simulations show that Huginn can relearn the structure of metabolic models, and that it can do it better than previous systems thanks to the specific features introduced in it. Furthermore, we show how the design of extended crucial experiments can be automated using Answer Set Programming for the first time.

Este trabalho e dedicado ao estudo da exploração de paralelismo na Programação em Lógica. O aspecto declarativo das linguagens de Programação em Lógica permite uma exploração eficiente do paralelismo implícito no código, de forma mais simples que as linguagens imperativas. Ao mesmo tempo, o paralelismo tem-se mostrado uma forte opção para procura de aumentos significativos do desempenho dos computadores. Como conseqüência, nos últimos anos, diversas maquinas paralelas tem surgido no mercado. No entanto, a sua efetiva utilização ainda ressente-se de uma dificuldade de programação maior que a das maquinas sequênciais. Por outro lado, o alto nível das linguagens de Programação em Lógica permite o desenvolvimento de programas de forma mais rápida e concisa do que as linguagens tradicionais (imperativas). Porem, apesar dos importantes progressos nas técnicas de compilação destas linguagens, elas permanecem menos eficientes que as linguagens imperativas. 0 aumento na eficiência de execução da Programação em Lógica, com o use do paralelismo, certamente estenderá o seu emprego. Em função disto, a unido da Programação em Lógica e maquinas paralelas tem sido proposta como uma alternativa para facilitar a programação das maquinas paralelas, bem como para aumentar o desempenho na Programação em Lógica. O ponto central do trabalho e a concepção de um modelo para exploração do paralelismo E Restrito na execução de Prolog, voltado para arquiteturas multiprocessadoras sem memória comum. Como ponto de partida foi utilizado o modelo já definido para exploração do paralelismo OU do projeto OPERA, do Instituto de Informática da UFRGS, de maneira que o modelo de paralelismo E proposto possa vir a compor, com aquele, uma plataforma que integre a exploração simultânea dos paralelismos E e OU. O modelo concebido compreende uma proposta de compilação e um ambiente de execução. A detecção e o controle do paralelismo é iniciado na compilação. Nesta fase, a gerada uma Expressão Condicional de Execução para cada clausula do programa Prolog, cuja avaliação em tempo de processamento determina a execução, em paralelo ou não, dos literais que compõem a clausula. A Maquina Abstrata Prolog, projetada para o emulador paralelo, é baseada na WAM (Warren Abstract Machine), uma das mais eficientes e difundidas técnicas para compilação Prolog. Isto, dentre outros aspectos, confere uma boa portabilidade ao modelo. O ambiente de execução compreende a concepção de uma arquitetura de processos formada por trabalhadores OPERA, uma filosofia de escalonamento de serviço entre estes trabalhadores, uma política para gerencia de sua memória e uma estratégia para as comunicações. Para validar o modelo proposto para exploração do paralelismo E, o mesmo foi implementado em rede local de estações Unix, obtendo bons resultados. / This work is devoted to the study of the exploration of parallelism in Logic Programming. The declarative aspect of the Logic Programming languages allows an efficient exploration of the implicit parallelism in the code, in a simpler form than the imperative languages. At the same time, parallelism has been shown as a strong option to the search for significant increases in the performance of the computers. As a consequence, in the last years, several parallel machines have been sprung up into the market. Nevertheless, their effective usefulness still undergoes some difficulties in programming which are greater than those of the sequential machines. On the other hand, the high level of Logic Programming languages allows programs development to be faster and concise than in the traditional languages (imperatives). However, despite the important progress in compiling techniques for these languages, they remain less efficient than the imperatives languages. The increase in execution efficiency of logic programs, with the use of parallelism, will probabily extend their use. Having this in mind, the union of the Logic Programming and parallel machines has been proposed as an alternative to make programming of the parallel machines easier, as well as to increase the performance of Logic Programming. The central aspect of the work is the conception of a model to explore the Restricted AND Parallelism in the execution of Prolog, turned to multiprocessing architectures without a common memory. As a starting point, the already defined model for exploring OR parallelism of the OPERA project, from the Instituto de Informatica da UFRGS was used. This happened so that the proposed model of AND parallelism can make up a plataform with that one to integrate the simultaneous exploration of the AND and OR parallelisms. The conceived model holds a proposal of compilation and execution environment. The detection and the control of the parallelism is started in the compilation. A Conditional Expression of Execution to each clause of the Prolog program is generated on this phase. Its evaluation, during the time of processing, determines the execution, whether or not in parallel, of the literals that constitute the clause. The Abstract Prolog Machine, projected for the parallel emulator, is based on the WAM (Warren Abstract Machine) which is one of the most efficient and spread techniques for Prolog compilation. This aspects, among others, gives a good portability to the model. The environmente of execution comprises the conception of an architecture of processes formed by OPERA workers and a philosophy of scheduling service among these workers; it also comprise a policy to manage its memory and a strategy for the communications. So that the proposed model for the exploitation of AND parallelism got validated, it was implemented on a local net of Unix workstations, obtaining good results.

Este trabalho apresenta um modelo de escalonamento hierárquico para exploração do paralelismo E Independente e do paralelismo OU na programação em lógica. O modelo utiliza informações de granulosidade geradas pelo GRANLOG (Granularity Analyzer for Logic Programming) para o auxílio ao escalonamento. Um estudo detalhado de ambientes de programação em lógica explorando o paralelismo é apresentado. A partir deste, é feita uma comparação destacando as principais características de cada um. O escalonamento em linhas gerais também é descrito e uma enfâse maior é dada ao escalonamento dinâmico. As principais vantagens e desvantagens de cada escalonador são mostradas. O modelo proposto recebe o nome de DSLP – Distributed Scheduler for Logic Programming e realiza o escalonamento em duas fases. Inicialmente é executada a Fase OU, na qual todo paralelismo OU é explorado. Em seguida, é iniciada a Fase E onde ocorre a exploração do paralelismo E Independente. A estratégia de escalonamento proposta, utiliza informações de complexidade do GRANLOG para determinar o trabalho a ser exportado, bem como o nível de sobrecarga dos nodos. Para validação do trabalho, um protótipo utilizando o ambiente Parallel Virtual Machine foi implementado. O protótipo é um simulador de programas Prolog e implementa a fase E de escalonamento. / This work presents a hierarchical scheduling model for exploration of the Independent AND parallelism and OR parallelism in logic programming. The model uses granularity information generated by GRANLOG (Granularity Analyzer for Logic Programming) to aid the scheduler. A detailed study of parallel logic programming environments is presented. Starting from this, it is made a comparison highlighting the main characteristics of each one. Scheduling in general is also described and the dynamic scheduling is pointed out. The main advantages and disadvantages of each scheduler are shown. The proposed model receives the name of DSLP – Distributed Scheduler for Logic Programming and it accomplishes the scheduling in two phases. Initially the OR Phase is executed and the whole OR parallelism is explored. Soon after, it is initiate the AND Phase with the exploration of the Independent AND parallelism. The scheduling strategy proposed uses complexity information generated by GRANLOG to determinate the task to be exported, as well as the nodes overloaded level. For work validation, a prototype using the Parallel Virtual Machine was implemented. The prototype is a Prolog simulator and it implements the scheduling AND phase.

Este trabalho a dedicado ao estudo da exploração de paralelismo OU na programação em lógica com restrições em ambientes distribuídos. A programação em lógica, cuja linguagem mais significativa 6 Prolog, tem como premissa a utilização da lógica de predicados como linguagem computacional. A programação em lógica com restrições (CLP) é uma extensão da programação em lógica, onde busca-se a eficiência e a possibilidade de executar novas classes de problemas. Variáveis em CLP podem pertencer a domínios específicos como, por exemplo, reais ou booleanos. O principal conceito introduzido é a restrição. Restrição a uma equação que representa uma certa informação sobre uma variável e a sua relação com outras variáveis. o uso de restrições foi proposto para diminuir o espaço de busca na execução dos programas. Apesar de mais eficientes que a programação em lógica clássica, para algumas aplicações reais o desempenho das linguagens CLP ainda é insatisfatório. Por isso, é necessário buscar alternativas novas como a execução em paralelo. A exploração de paralelismo implícito em programas em 1ógica já demonstrou resultados promissores. Vários modelos foram propostos e implementados utilizando as duas principais fontes de paralelismo — E e OU — de forma isolada ou combinada. O objetivo principal desse trabalho é apresentar o modelo pclp(FD) de exploração de paralelismo OU multi-sequêncial para um ambiente com memória distribuída. O modelo pclp(FD) caracteriza-se pela existência de vários trabalhadores, cada um deles possuindo uma maquina abstrata completa. O escalonamento de tarefas a realizado por uma política dinâmica e distribuída. Uma tarefa em pclp(FD) equivale a um ponto de escolha e a um contexto de execução. O contexto de execução a formado por porções da pilha do exportador. Para que o importador tenha acesso ao contexto de execução utiliza-se a cópia incremental, que a uma das varias técnicas possíveis. Cada trabalhador possui a sua própria copia privada das pilhas de execução. A cópia caracteriza-se pelo envio das pilhas de execução do exportador para uma área privada do importador. A cópia incremental é uma técnica mais otimizada que verifica a existência de partes comuns entre os trabalhadores, copiando apenas as panes novas. O algoritmo de cópia incremental proposto no modelo a feito sem nenhuma centralização de informação do estado das pilhas. O projeto e implementação de um prot6tipo para esse modelo, utilizando a linguagem clp(FD), que implementa CLP sobre domínios finitos, permitirá uma analise das vantagens e desvantagens do modelo proposto. Os resultados obtidos com a análise servirão de base para trabalhos futuros, visando aprimorar a implementação e o modelo. / This work is dedicated to the study of the exploration of OR parallelism in Constraint Logic Programming for distributed environment. Logic Programming, which the most meaningful language is Prolog, has as premise the use of the logic of predicates as computational language. Constraint Logic Programming or CLP is an extension of the logic programming, where efficiency and the possibility to execute new kinds of problems are searched. A variable in CLP can belong to specific domains as, for example, Real or Boolean. The main concept introduced is the constraint. Constraint is an equation that represents a certain information over a variable and its relation with others variables. The use of constraints was proposed to decrease search space in the program execution. Although it is more efficient than classic logic programming, for some real applications, the performance of CLP languages still is unsatisfactory. So, it is necessary to search alternatives as parallel execution. The exploration of implicit parallelism in programs in logic has already demonstrated promising results. Several models have been proposed and implemented using the two main sources of parallelism - AND and OR — in an isolated or combined form. The main objective of this work is to present the pclp(FD) model of exploration of multi-sequential OR parallelism for a distributed memory environment. The pclp(FD) model is characterized for the existence of some workers, each one of them possessing a complete abstract machine. Task scheduling is executed by one dynamic and distributed policy. A task in pclp(FD) is equivalent to a choice point and an execution context. Execution context is formed by portions of the stack of the exporter. So that importer has access to the execution context, it uses incremental copy, which is one of the several possible techniques. The copy is characterized for sending execution stacks of the exporter to a private area of the importer, that is, each worker possesses its private copy of the execution stacks. The incremental copy is a more optimized technique that verifies the existence of common parts between workers, copying only the new ones. The incremental copy algorithm proposed in the model executes without centralized information of the state of the stacks. A prototype project and implementation for this model, using the language clp(FD), that implements CLP over finite domains, will allow an analysis of advantages and disadvantages of the considered model. The results gotten with the analysis will serve of base for future works, aiming to improve the implementation and the model.

Artificial intelligence and machine learning are fields of research that have become very popular and are getting more attention in the media as our computational power increases and the theories and latest developments of these fields can be put into practice in the real world. The field of machine learning consists of different paradigms, two of which are the symbolic and connectionist paradigms. In 1991 it was pointed out by Minsky that we could benefit from sharing ideas between the paradigms instead of competing for dominance in the field. That is why this thesis is investigating two approaches to inductive logic programming, where the main research goals are to, first: find similarities or differences between the approaches and potential areas where cross-pollination could be beneficial, and secondly: investigate their relative performance to each other based on the results published in the research. The approaches investigated are Meta-Interpretive Learning and Inductive Metalogic Programming, which belong to the symbolic paradigm of machine learning. The research is conducted through a comparative study based on published research papers. The conclusion to the study suggests that at least two aspects of the approaches could potentially be shared between them, namely the reversible aspect of the meta-interpreter and restricting the hypothesis space using the Herbrand base. However, the findings regarding performance were deemed incompatible, in terms of a fair one to one comparison. The results of the study are mainly specific, but could be interpreted as motivation for similar collaboration efforts between different paradigms.

<p>Temporal Action Logic is a well established logical formalism for reasoning about action and change that has long been used as a formal specification language. Its first-order characterization and explicit time representation makes it a suitable target for automated theorem proving and the application of temporal constraint solvers. We introduce a translation from a subset of Temporal Action Logic to constraint logic programs that takes advantage of these characteristics to make the logic applicable, not just as a formal specification language, but in solving practical reasoning problems. Extensions are introduced that enable the generation of action sequences, thus paving the road for interesting applications in deductive planning. The use of qualitative temporal constraints makes it possible to follow a least commitment strategy and construct partially ordered plans. Furthermore, the logical language and logic program translation is extended with the notion of composite actions that can be used to formulate and execute scripted plans with conditional actions, non-deterministic choices, and loops. The resulting planner and reasoner is integrated with a graphical user interface in our autonomous helicopter research system and applied to logistics problems. Solution plans are synthesized together with monitoring constraints that trigger the generation of recovery actions in cases of execution failures.</p>

This thesis implements a logical formalism framework called Concurrent Constraint Transaction Logic (abbr.,CCTR) which was defined for modeling and scheduling of workflows under resource allocation and cost constraints and develops an extensible and flexible graphical user interface for the framework. CCTR extends Concurrent Transaction Logic and integrates with Constraint Logic Programming to find the correct scheduling of tasks that involves resource and cost constraints. The developed system, which integrates Prolog and Java Platforms, is designed to serve as the basic environment for enterprise applications that involves CCTR based workflows and schedulers. Full implementation described in this thesis clearly illustrated that CCTR can be used as a workflow scheduler that involves not only temporal and causal constraints but also resource and cost constraints.