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.

The goal of this project is to design a tool to help users translate any logic statement into Disjunctive Normal Form and present the result as an AND/OR TABLE, which makes the logic relation easier to express by using a two-dimensional grid of values or expressions. This tool is implemented through a web-based and Java-based application. Thus, the user can utilize this tool via World Wide Web.

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.

Timing hazards are common problems found in logic circuits. A new integrated hazard detection system (HDS), which is implemented in VHDL, is proposed to detect the static, the dynamic, and the function hazards in any logic circuit that is described structurally in VHDL. This system adopts the IEEE VHDL Model Standard Group 1076-1164 Nine-Valued Multiple-Valued Logic package. Without any designer-supplied arbitrary input test patterns, the system predicts which input combinations will cause hazards, reports what type of hazards, and provides detailed timing information on the hazards in the combinational logic circuit with fixed gate delays. / Master of Science

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)

Submitted by Milena Rubi ( ri.bso@ufscar.br) on 2017-10-17T14:08:43Z No. of bitstreams: 1 GONÇALVES_JUNIOR_Clenio_2017.pdf: 4502082 bytes, checksum: 36fad22cf5caad0d975a2df1fe5e7a55 (MD5) / Approved for entry into archive by Milena Rubi ( ri.bso@ufscar.br) on 2017-10-17T14:08:53Z (GMT) No. of bitstreams: 1 GONÇALVES_JUNIOR_Clenio_2017.pdf: 4502082 bytes, checksum: 36fad22cf5caad0d975a2df1fe5e7a55 (MD5) / Approved for entry into archive by Milena Rubi ( ri.bso@ufscar.br) on 2017-10-17T14:09:02Z (GMT) No. of bitstreams: 1 GONÇALVES_JUNIOR_Clenio_2017.pdf: 4502082 bytes, checksum: 36fad22cf5caad0d975a2df1fe5e7a55 (MD5) / Made available in DSpace on 2017-10-17T14:09:10Z (GMT). No. of bitstreams: 1 GONÇALVES_JUNIOR_Clenio_2017.pdf: 4502082 bytes, checksum: 36fad22cf5caad0d975a2df1fe5e7a55 (MD5) Previous issue date: 2017-02-06 / Não recebi financiamento / Knowledge representation process is an essential matter regarding Computer Music systems. Methods have been applied in order to provide computers with the capability to generate conclusions based on experience in specialized domains. Inductive Logic Programming is a research field which combines concepts of Logic Programming and Machine Learning. Due to its declarative feature, both acquired and produced knowledge can be presented to not-expert users in a naturally understandable way. This work deals with Music Knowledge Representation from the perspective of multi- paradigm programming, using Inductive Logic Programming technique and including the development of the knowledge-based music system Fraseado. Finally, a method for the evaluation of algorithmic composition systems - the Expanded Turing Test - is presented. / O processo de representação de conhecimento em Computação Musical constitui um elemento essencial para o desenvolvimento de sistemas. Métodos têm sido aplicados visando fornecer ao computador a capacidade de inferir informações a partir da experiência e definições previamente estabelecidas. Neste sentido, a Programação Lógica Indutiva apresenta-se como um crescente campo de pesquisa que incorpora conceitos de Programação em Lógica e Aprendizado de Máquina. O presente trabalho aborda a Representação de Conhecimento Musical sob a ótica da programação multiparadigma, com uso da técnica de Programação Lógica Indutiva. Inclui o desenvolvimento do sistema musical baseado em conhecimento Fraseado. Por fim é apresentado um método para avaliação de sistemas de composição algorítmica - o Teste de Turing Expandido.

Traditional Inductive Logic Programming (ILP) focuses on the setting where the target theory is a generalisation of the observations. This is known as Observational Predicate Learning (OPL). In the Theory Completion setting the target theory is not in the same predicate as the observations (non-OPL). This thesis investigates two alternative simple extensions to traditional ILP to perform non-OPL or Theory Completion. Both techniques perform extraction-case abduction from an existing background theory and one seed observation. The first technique -- Logical Back-propagation -- modifies the existing background theory so that abductions can be achieved by a form of constructive negation using a standard SLD-resolution theorem prover. The second technique -- SOLD-resolution -- modifies the theorem prover, and leaves the existing background theory unchanged. It is shown that all abductions produced by Logical Back-propagation can also be generated by SOLD-resolution; but the reverse does not hold. The implementation using the SOLD-resolution technique -- the ALECTO system -- was applied to the problems of completing context free and context dependant grammars; and learning Event Calculus programs. It was successfully able to learn an Event Calculus program to control the navigation of a real-life robot. The Event Calculus is a formalism to represent common-sense knowledge. It follows that the discovery of some common-sense knowledge was produced with the assistance of a machine.