A disciplina de Lógica para Computação faz parte da maioria dos cursos de Tecnologia da Informação e Comunicação. O sistema de Dedução Natural é amplamente utilizado para o ensino de demonstrações e este conteúdo consta em muitos dos livros-texto de Lógica. Este trabalho apresenta um assistente de provas, NADIA (Natural Deduction Proof Assistant), para o sistema de Dedução Natural em Lógica Proposicional, no estilo de Fitch (caixas), com a finalidade de auxiliar no ensino-aprendizagem de estudantes de graduação e pós-graduação. NADIA permite que os estudantes escrevam suas demonstrações de forma mais próxima possível das provas que realizam no papel. NADIA verifica automaticamente se a demonstração está correta e, caso contrário, exibe os erros encontrados. Para avaliar a experiência dos estudantes no uso do NADIA realizamos uma avaliação da ferramenta com alunos de cinco turmas da disciplina de Lógica para Computação que foram ofertadas em 2021.1 e 2021.2.

This work presents the system ANITA (Analytic Tableau Proof Assistant) developed for teaching analytic tableaux to computer science students. The tool is written in Python and can be used as a desktop application, or in a web platform. This paper describes the logical system of the tool, explains how the tool is used and compares it to several similar tools. ANITA has already been used in logic courses and an evaluation of the tool is presented.

Automatic Speech Recognition (ASR) is an essential task for many applications like automatic caption generation for videos, voice search, voice commands for smart homes, and chatbots. Due to the increasing popularity of these applications and the advances in deep learning models for transcribing speech into text, this work aims to evaluate the performance of commercial solutions for ASR that use deep learning models, such as Facebook Wit.ai, Microsoft Azure Speech, and Google Cloud Speech-to-Text. The results demonstrate that the evaluated solutions slightly differ. However, Microsoft Azure Speech outperformed the other analyzed APIs.

In this work, we present a logic based on first-order CTL, namely Game Analysis Logic (GAL), in order to reason about games. We relate models and solution concepts of Game Theory as models and formulas of GAL, respectively. Precisely, we express extensive games with perfect information as models of GAL, and Nash equilibrium and subgame perfect equilibrium by means of formulas of GAL. From a practical point of view, we provide a GAL model checker in order to analyze games automatically. We use our model checker in at least two directions: to find solution concepts of Game Theory; and, to analyze players that are based on standard algorithms of the AI community, such as the minimax procedure.

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