[pt] Esta dissertação apresenta uma integração entre a programação funcional e a programação em lógica, obtida pela definição e implementação da Linguagem LispLog. Nesta nova linguagem, o resultado de uma resolução pode ser utilizado como argumento de uma função (pelo operador metalisp) e o resultado da avaliação de uma função pode ser ligado a uma variável lógica (pelo operador avalia). A construção desta linguagem foi realizada a partir da simulação, em microcomputador similar ao IBM-PC, de uma máquina com arquitetura de pilhas, chamada Máquina LispLog, e de seu compilador. A utilização desta linguagem é possível através do Sistema LispLog, que fornece um ambiente de programação orientado por menus. / [en] This dissertation shows an integration between the function programming and logic programming, achieved through LispLog Language’s definition and implementation. In this new language the resultant of a resolution may be used as an argument of a function (through metalisp operator) and the result of a function’s avaliation may be linked to a logic variable (through avalia operator). The LispLog Language was constructed by a simulation of stack architecture machine, named LispLog Machine, and its compiler, in a microcomputer similar similar to IBM-PC. The LispLog System provides a programming environment oriented by menus, wich makes possible the use of this language

Model checking is a formal verification method where one tries to prove or disprove properties of a formal system. Typical systems one might want to prove properties within are network protocols and digital circuits. Typical properties to check for are safety (nothing bad ever happens) and liveness (something good eventually happens). This thesis describes an implementation of a sound and complete model checker for Computation Tree Logic (CTL) using Constraint Logic Programming over Finite Domains (CLP(FD)). The implementation described uses tabled resolution to remember earlier computations, is parameterised by choices of computation strategies and can with slight modification support different constraint domains. Soundness under negation is maintained through a restricted form of constructive negation. The computation process amounts to a fixpoint search, where a fixpoint is reached when no more extension operations has any effect. As results show, the choice of strategies does influence the efficiency of the computation. Soundness and completeness are of course independent of the choice of strategies. Strategies include how to choose the extension operation for the next step and whether to perform global or local rule instantiations, resulting in bottom-up or top-down computations respectively.

In an ever faster changing environment, software developers not only need agile methods, but also agile programming paradigms and tools. A paradigm shift towards declarative programming has begun; a clear indication of this is Microsoft's substantial investment in functional programming. Moreover, several attempts have been made to enable visual programming. We believe that software development is ready for a new paradigm which goes beyond any existing declarative paradigm: visual compositional-relational programming. Compositional-relational programming (CRP) is a purely declarative paradigm -- making it suitable for a visual representation. All procedural aspects -- including the increasingly important issue of parallelization -- are removed from the programmer's consideration and handled in the underlying implementation. The foundation for CRP is a theory of higher-order combinatory logic programming developed by Hamfelt and Nilsson in the 1990's. This thesis proposes a model for visualizing compositional-relational programming. We show that the diagrams are isomorphic with the programs represented in textual form. Furthermore, we show that the model can be used to automatically generate code from diagrams, thus paving the way for a visual integrated development environment for CRP, where programming is performed by combining visual objects in a drag-and-drop fashion. At present, we implement CRP using Prolog. However, in future we foresee an implementation directly on one of the major object-oriented frameworks, e.g. the .NET platform, with the aim to finally launch relational programming into large-scale systems development.

PrologPF is a parallelising compiler targeting a distributed system of general purpose workstations connected by a relatively low performance network. The source language extends standard Prolog with the integration of higher-order functions. The execution of a compiled PrologPF program proceeds in a similar manner to standard Prolog, but uses oracles in one of two modes. An oracle represents the sequence of clauses used to reach a given point in the problem search tree, and the same PrologPF executable can be used to build oracles, or follow oracles previously generated. The parallelisation strategy used by PrologPF proceeds in two phases, which this research shows can be interleaved. An initial phase searches the problem tree to a limited depth, recording the discovered incomplete paths. In the second phase these paths are allocated to the available processors in the network. Each processor follows its assigned paths and fully searches the referenced subtree, sending solutions back to a control processor. This research investigates the use of the technique with a one-time partitioning of the problem and no further scheduling communication, and with the recursive application of the partitioning technique to effect dynamic work reassignment. For a problem requiring all solutions to be found, execution completes when all the distributed processors have completed the search of their assigned subtrees. If one solution is required, the execution of all the path processors is terminated when the control processor receives the first solution. The presence of the extra-logical Prolog predicate cut in the user program conflicts with the use of oracles to represent valid open subtrees. PrologPF promotes the use of higher-order functional programming as an alternative to the use of cut. The combined language shows that functional support can be added as a consistent extension to standard Prolog.

O Aprendizado de Máquina trata da questão de como desenvolver programas de computador capazes de aprender um conceito ou hipótese a partir de um conjunto de exemplos ou casos observados. Baseado no conjunto de treinamento o algoritmo de aprendizado induz a classificação de uma hipótese capaz de determinar corretamente a classe de novos exemplos ainda não rotulados. Linguagens de descrição são necessárias para escrever exemplos, conhecimento do domínio bem como as hipóteses aprendidas a partir dos exemplos. Em geral, essas linguagens podem ser divididas em dois tipos: linguagem baseada em atributo-valor ou proposicional e linguagem relacional. Algoritmos de aprendizado são classificados como proposicional ou relacional dependendo da liguagem de descrição que eles utilizam. Além disso, no aprendizado simbólico o objetivo é gerar a classificação de hipóteses que possam ser facilmente interpretadas pelos humanos. Algoritmos de aprendizado proposicional utilizam a representação atributo-valor, a qual é inadequada para representar objetos estruturados e relações entre esses objetos. Por outro lado, a Programação lógica Indutiva (PLI) é realizada com o desenvolvimento de técnicas e ferramentas para o aprendizado relacional. Sistemas de PLI são capazes de aprender levando em consideração conhecimento do domínio na forma de um programa lógico e também usar a linguagem de programas lógicos para descrever o conhecimento induzido. Neste trabalho foi implementado um módulo chamado Kaeru para converter dados no formato atributo-valor para o formato relacional utilizado pelo sistema de PLI Aleph. Uma série de experimentos foram realizados com quatro conjuntos de dados naturais e um conjunto de dados real no formato atributo valor. Utilizando o módulo conversor Kaeru esses dados foram convertidos para o formato relacional utilizado pelo Aleph e hipóteses de classificação foram induzidas utilizando aprendizado proposicional bem como aprendizado relacional. É mostrado também, que o aprendizado proposicional pode ser utilizado para incrementar o conhecimento do domínio utilizado pelos sistemas de aprendizado relacional para melhorar a qualidade das hipóteses induzidas. / Machine Learning addresses the question of how to build computer programs that learn a concept or hypotheses from a set of examples, objects or cases. Descriptive languages are necessary in machine learning to describe the set of examples, domain knowledge as well as the hypothesis learned from these examples. In general, these languages can be divided into two types: languages based on attribute values, or em propositional languages, and relational languages. Learning algorithms are often classified as propositional or relational taking into consideration the descriptive language they use. Typical propositional learning algorithms employ the attribute value representation, which is inadequate for problem-domains that require reasoning about the structure of objects in the domain and relations among such objects. On the other hand, Inductive Logig Programming (ILP) is concerned with the development of techniques and tools for relational learning. ILP systems are able to take into account domain knowledge in the form of a logic program and also use the language of logic programs for describing the induced knowledge or hypothesis. In this work we propose and implement a module, named kaeru, to convert data in the attribute-value format to the relational format used by the ILP system Aleph. We describe a series of experiments performed on four natural data sets and one real data set in the attribute value format. Using the kaeru module these data sets were converted to the relational format used by Aleph and classifying hipoteses were induced using propositional as well as relational learning. We also show that propositional knowledge can be used to increment the background knowledge used by relational learners in order to improve the induded hypotheses quality.

This Master's deals with the implementation of the interpreter of logic programming language "Prolog". It summarises the different approaches to evaluation of programs in thislanguage with focus on description of The Warren Abstract Machine. A new way of integratingProlog into The Microsoft .NET platform has been designed as well as its connectionwith object-oriented languages. Subsequently, an interpreter and a compiler based on TheWarren Abstract Machine have been designed and implemented including the connectionto The Microsoft.NET platform.

This paper describes automated examination scheduling for the Faculty of Information Technology of Brno University of Technology. It specifies a list of restrictions that must by satisfied. Furthermore, this limitations are classified due to their influence on a quality of the final version of the examination schedule. There are two types of restrictions; soft and hard. The task is to find such a solution that satisfies all hard constraints and breaks the minimum of soft constraints using techniques described in this paper.

abstract: While in recent years deep learning (DL) based approaches have been the popular approach in developing end-to-end question answering (QA) systems, such systems lack several desired properties, such as the ability to do sophisticated reasoning with knowledge, the ability to learn using less resources and interpretability. In this thesis, I explore solutions that aim to address these drawbacks. Towards this goal, I work with a specific family of reading comprehension tasks, normally referred to as the Non-Extractive Reading Comprehension (NRC), where the given passage does not contain enough information and to correctly answer sophisticated reasoning and ``additional knowledge" is required. I have organized the NRC tasks into three categories. Here I present my solutions to the first two categories and some preliminary results on the third category. Category 1 NRC tasks refer to the scenarios where the required ``additional knowledge" is missing but there exists a decent natural language parser. For these tasks, I learn the missing ``additional knowledge" with the help of the parser and a novel inductive logic programming. The learned knowledge is then used to answer new questions. Experiments on three NRC tasks show that this approach along with providing an interpretable solution achieves better or comparable accuracy to that of the state-of-the-art DL based approaches. The category 2 NRC tasks refer to the alternate scenario where the ``additional knowledge" is available but no natural language parser works well for the sentences of the target domain. To deal with these tasks, I present a novel hybrid reasoning approach which combines symbolic and natural language inference (neural reasoning) and ultimately allows symbolic modules to reason over raw text without requiring any translation. Experiments on two NRC tasks shows its effectiveness. The category 3 neither provide the ``missing knowledge" and nor a good parser. This thesis does not provide an interpretable solution for this category but some preliminary results and analysis of a pure DL based approach. Nonetheless, the thesis shows beyond the world of pure DL based approaches, there are tools that can offer interpretable solutions for challenging tasks without using much resource and possibly with better accuracy. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2019

Since Quantum Computer is almost realizable on large scale and Quantum Technology is one of the main solutions to the Moore Limit, Quantum Logic Synthesis (QLS) has become a required theory and tool for designing Quantum Logic Circuits. However, despite its growth, there is no any unified aproach to QLS as Quantum Computing is still being discovered and novel applications are being identified. The intent of this study is to experimentally explore principles of Quantum Logic Synthesis and its applications to Inductive Machine Learning. Based on algorithmic approach, I first design a Genetic Algorithm for Quantum Logic Synthesis that is used to prove and verify the methods proposed in this work. Based on results obtained from the evolutionary experimentation, I propose a fast, structure and cost based exhaustive search that is used for the design of a novel, least expensive universal family of quantum gates. The results form both the evolutionary and heuristic search are used to formulate an Inductive Learning Approach based on Quantum Logic Synthesis with the intended application being the humanoid behavioral robotics. The presented approach illustrates a successful algorithmic approach, where the search algorithm was able to invent/discover novel quantum circuits as well as novel principles in Quantum Logic Synthesis.

This thesis presents the Planning System DLVK, which supports the novel Planning Language K. The language allows to represent AI planning problems in a declarative way and is capable of representing incomplete knowledge as well as nondeterministic effects of actions.After explaining some basics, the syntax and semantics of this language will be formally described and some results on the computational complexity of our language will be given, proving that K is capable of expressing hard planning problems, possibly involving incomplete knowledge or uncertainty, such as secure (conformant) planning.A translation from various planning tasks specified in K to a logic programming framework will be shown subsequently. We have implemented a prototype of a planning system, DLVK, on top of the disjunctive logic programming system DLV, to show the practical use of our translation. This prototype will be presented in detail. Finally, examples and experimental results will be given, together with an outlook to further research. / Austrian Science Funds (FWF)