Percorrer por autor "Fachada, Nuno"
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Item Active learning prototypes for teaching game AI(IEEE, 2023-08-21) Fachada, Nuno; Barreiros, Filipa; Lopes, Phil; Fonseca, MicaelaArtificial intelligence (AI) in computer games can enhance the player experience by providing more realistic and dynamic interactions with non-player characters and/or the game environment and is, therefore, an essential skill for game development students to acquire. In this paper, we discuss ten active learning prototypes for undergraduate game development students focusing on AI for Games. The prototypes were implemented in the Unity game engine, and each prototype considers a particular technique or set of algorithms. Depending on the prototype, students are required to interact with it on two levels: 1) by running it within the Unity editor, manipulating the respective technique's parameters, and experimenting and/or playing with the implemented demo or game; or, 2) in addition to the previous level, by actively changing and expanding the provided code to achieve the desired behavior or result. We performed a survey immediately after contact with the prototypes and found that they were easy for the students to manipulate and/or build upon, and most significantly, that they helped students understand the associated techniques and algorithms.Item Assessing the feasibility of OpenCL CPU implementations for agent-based simulations(AGENT-BASED MODELING, 2017-05) Fachada, Nuno; Rosa, Agostinho C.Agent-based modeling (ABM) is a bottom-up modeling approach, where each entity of the system being modeled is uniquely represented as a self-determining agent. Large scale emergent behavior in ABMs is population sensitive. As such, it is advisable that the number of agents in a simulation is able to reflect the reality of the system being modeled. This means that in domains such as social modeling, ecology, and biology, systems can contain millions or billions of individuals. Such large scale simulations are only feasible in non-distributed scenarios when the computational power of commodity processors, such as GPUs and multi-core CPUs, is fully exploited. In this paper we evaluate the feasibility of using CPU-oriented OpenCL for high-performance simulations of agent-based models. We compare a CPU-oriented OpenCL implementation of a reference ABM against a parallel Java version of the same model. We show that there are considerable gains in using CPU-based OpenCL for developing and implementing ABMs, with speedups up to 10x over the parallel Java version on a 10-core hyper-threaded CPU.Item Automated Generation of Map Pieces for Snappable Meshes(ACM, 2023-04-12) Andrade, Diogo de; Fachada, NunoSnappable Meshes is an algorithm that procedurally generates 3D environments by iteratively selecting and linking pre-built map pieces. These pieces are triangular meshes annotated by designers with connectors marking potential links, and bounding volumes indicating where overlaps should be avoided. In this article, we present a method for automatically generating connectors and bounding volumes from generic non-manifold triangular meshes for use with the Snappable Meshes algorithm, minimizing artist/designer work, while encouraging iteration of map piece design, an essential part of successful environment generation.Item Biofeedback Game Design(2018) Luz, Filipe Costa; Fachada, Nuno; Junior, RobertoThis project aims to explore the psychological effect of the player during gaming and how reciprocally game designers can manipulate the sensations of the player through biofeedback information (physiological sensors) and implementation of certain game mechanics.Item cf4ocl: A C framework for OpenCL(Elsevier, 2017) Fachada, Nuno; Lopes, Vitor V.; Martins, Rui C.; Rosa, Agostinho C.OpenCL is an open standard for parallel programming of heterogeneous compute devices, such as GPUs, CPUs, DSPs or FPGAs. However, the verbosity of its C host API can hinder application development. In this paper we present cf4ocl, a software library for rapid development of OpenCL programs in pure C. It aims to reduce the verbosity of the OpenCL API, offering straightforward memory management, integrated profiling of events (e.g., kernel execution and data transfers), simple but extensible device selection mechanism and user-friendly error management. We compare two versions of a conceptual application example, one based on cf4ocl, the other developed directly with the OpenCL host API. Results show that the former is simpler to implement and offers more features, at the cost of an effectively negligible computational overhead. Additionally, the tools provided with cf4ocl allowed for a quick analysis on how to optimize the application.Item ColorShapeLinks: A board game AI competition for educators and students(Elsevier, 2021-02-23) Fachada, NunoColorShapeLinks is an AI board game competition framework specially designed for students and educators in videogame development, with openness and accessibility in mind. The competition is based on an arbitrarily-sized version of the Simplexity board game, the motto of which, “simple to learn, complex to master”, is curiously also applicable to AI agents. ColorShapeLinks offers graphical and text-based frontends and a completely open and documented development framework built using industry standard tools and following software engineering best practices. ColorShapeLinks is not only a competition, but both a game and a framework which educators and students can extend and use to host their own competitions. It has been successfully used for running internal competitions in AI classes, as well as for hosting an international AI competition at the IEEE Conference on Games.Item A computational pipeline for modeling and predicting wildfire behavior(SciTePress, 2022-04-24) Fachada, NunoWildfires constitute a major socioeconomic burden. While a number of scientific and technological methods have been used for predicting and mitigating wildfires, this is still an open problem. In turn, agent-based modeling is a modeling approach where each entity of the system being modeled is represented as an independent decision-making agent. It is a useful technique for studying systems that can be modeled in terms of interactions between individual components. Consequently, it is an interesting methodology for modeling wildfire behavior. In this position paper, we propose a complete computational pipeline for modeling and predicting wildfire behavior by leveraging agent-based modeling, among other techniques. This project is to be developed in collaboration with scientific and civil stakeholders, and should produce an open decision support system easily extendable by stakeholders and other interested parties. Keywords: Agent-based Modeling, High-performance Computing, Computational Intelligence, Verification and Validation, Wildfires.Item Desafios no Ensino de Programação a Alunos de Videojogos(Sociedade Portuguesa de Ciências dos Videojogos, 2020-05) Fachada, NunoAs unidades curriculares (UCs) de programação num curso de Videojogos devem ter dois objetivos: 1) um objetivo mais geral, que consiste em fornecer aos alunos as bases que lhes permitam assimilar conceitos gerais de programação, matemática e física, bem como desenvolver o seu pensamento lógico e algorítmico; e, 2) um objetivo mais específico, que consiste na aprendizagem das ferramentas e conceitos concretos que permitam aos alunos trabalhar de forma fluída no game engine de eleição do curso. Este último objetivo enquadra as UCs de programação numa lógica top-down, pois a seleção do game engine guia a forma como os respetivos programas são preparados. Dentro desta perspetiva, as UCs de Programação devem alimentar e ter em vista possíveis colaborações com as restantes UCs, em especial as de game development puro. De forma transversal, os exemplos de aula e projetos de avaliação devem estar devidamente adaptados aos alunos em questão, de modo a tornar a exposição das matérias o mais apelativa possível. Neste documento discutiremos a forma como estes desafios estão a ser abordados na Licenciatura em Videojogos da Universidade Lusófona de Humanidades e Tecnologias.Item Drop Project: an automatic assessment tool for programming assignments(Elsevier, 2022-06-01) Cipriano, Bruno Pereira; Fachada, Nuno; Alves, PedroAutomated assessment tools (AATs) are software systems used in teaching environments to automate the evaluation of computer programs implemented by students. These tools can be used to stimulate the interest of computer science students in programming courses by providing quick feedback on their work and highlighting their mistakes. Despite the abundance of such tools, most of them are developed for a specific course and are not production-ready. Others lack advanced features that are required for certain pedagogical goals (e.g. Git integration) and/or are not flexible enough to be used with students having different computer literacy levels, such as first year and second year students. In this paper we present Drop Project (DP), an automated assessment tool built on top of the Maven build automation software. We have been using DP in our teaching activity since 2018, having received more than fifty thousand submissions between projects, classroom exercises, tests and homework assignments. The tool’s automated feedback has allowed us to raise the difficulty level of the course’s projects, while the grading process has become more efficient and consistent between different teachers. DP is an extensively tested, production-ready tool. The software’s code and documentation are available in GitHub under an open-source software license. Keywords: Automated assessment ;Computer science education; Programming education ; Unit testingItem Enlarged PLIN5-uncoated lipid droplets in inner regions of skeletal muscle type II fibers associate with type 2 diabetes(Elsevier, 2022-02) Fachada, Vasco; Rahkila, Paavo; Fachada, Nuno; Turpeinen, Tuomas; Kujala, Urho M.; Kainulainen, HeikkiSkeletal muscle physiology remains of paramount importance in understanding insulin resistance. Due to its high lipid turnover rates, regulation of intramyocellular lipid droplets (LDs) is a key factor. Perilipin 5 (PLIN5) is one of the most critical agents in such regulation, being often referred as a protector against lipotoxicity and consequent skeletal muscle insulin resistance. We examined area fraction, size, subcellular localization and PLIN5 association of LDs in two fiber types of type 2 diabetic (T2D), obese (OB) and healthy (HC) individuals by means of fluorescence microscopy and image analysis. We found that T2D type II fibers have a significant sub-population of large and internalized LDs, uncoated by PLIN5. Based on this novel result, additional hypotheses for the pathophysiology of skeletal muscle insulin resistance are formulated, together with future research directions. Keywords: Lipid droplets, PLIN5, Type II diabetes, Skeletal muscle, Insulin resistance, Fiber typeItem Fun Maths for All Game Development Students(ACM, 2020-06) Andrade, Diogo de; Fachada, NunoWe present an approach for teaching maths to game development undergraduates from a wide variety of backgrounds. We discuss the concepts behind an introductory maths course, its assessment and put forward some preliminary results on a practical implementation.Item generateData—A 2D data generator(Elsevier, 2020-05) Fachada, Nuno; Rosa, Agostinho C.generateData is a MATLAB/Octave function for generating 2D data clusters. Data is created along straight lines, which can be more or less parallel depending on the selected input parameters. The function also allows to fine-tune the generated data with respect to number of clusters, total data points, average cluster separation and several other distributional properties.Item Generating multidimensional clusters with support lines(Elsevier, 2023) Fachada, Nuno; de Andrade, DiogoSynthetic data is essential for assessing clustering techniques, complementing and extending real data, and allowing for more complete coverage of a given problem’s space. In turn, synthetic data generators have the potential of creating vast amounts of data – a crucial activity when real-world data is at premium – while providing a well-understood generation procedure and an interpretable instrument for methodically investigating cluster analysis algorithms. Here, we present Clugen, a modular procedure for synthetic data generation, capable of creating multidimensional clusters supported by line segments using arbitrary distributions. Clugen is open source, comprehensively unit tested and documented, and is available for the Python, R, Julia, and MATLAB/Octave ecosystems. We demonstrate that our proposal can produce rich and varied results in various dimensions, is fit for use in the assessment of clustering algorithms, and has the potential to be a widely used framework in diverse clustering-related research tasks. Keywords: Synthetic data, Clustering, Data generation, Multidimensional dataItem Generative art with swarm landscapes(MDPI, 2020) Andrade, Diogo de; Fachada, Nuno; Fernandes, Carlos M.; Rosa, Agostinho C.We present a generative swarm art project that creates 3D animations by running a Particle Swarm Optimization algorithm over synthetic landscapes produced by an objective function. Different kinds of functions are explored, including mathematical expressions, Perlin noise-based terrain, and several image-based procedures. A method for displaying the particle swarm exploring the search space in aesthetically pleasing ways is described. Several experiments are detailed and analyzed and a number of interesting visual artifacts are highlighted.Item Humans vs AI : an exploratory study with online and offline learners(Springer Nature, 2024-01-02) Inácio, João; Fachada, Nuno; Carvalho, João P. Matos; Fernandes, Carlos M.We present an exploratory study comparing human player performance against online and offline AI learning techniques—the Naive Bayes Classifier and Genetic Algorithms, respectively—using a simple turn-based game. Human player performance is also assessed according to gender, age, experience playing games, and boredom level during game sessions. Human players and AI techniques are shown to obtain statistically equivalent score distributions. No gender performance differences were found, although performance seems to decrease with age. To a lesser extent, performance appears to improve with self-assessed experience and boredom levels. This study offers a base for more comprehensive experiments, suggesting various directions for future research.Item Improving the CS Curriculum of a Top-Down Videogames BA(ACM, 2022-11-17) Fachada, Nuno; de Andrade, Diogo; Serra, Pedro; Códices, Nélio; Luz, Filipe; Lopes, Phil; Fonseca, Micaela; Neves, PedroWe present a targeted curricular improvement on an interdisciplinary Videogames Bachelor of Arts, to be implemented in the 2022/23 academic year. The aim is to solve student adaptation issues with the program’s interdisciplinarity that were heightened by the COVID-19 pandemic.Item A method for detecting statistically significant differences in EEG data(OHBM, 2017) Fachada, Nuno; Cruz, Janir R. da; Herzog, Michael H.; Figueiredo, Patrícia; Rosa, Agostinho C.The highly multivariate nature of EEG data often limits the search for statistically significant differences in data collected from two or more groups of subjects. We have recently developed a new technique for assessing whether two or more multidimensional samples are drawn from the same distribution. Here, we apply this to EEG data collected from schizophrenia patients and healthy controls while performing a Visual Backward Masking (VBM) task.Item micompm: A MATLAB/Octave toolbox for multivariate independent comparison of observations(JOSS, 2018) Fachada, Nuno; Rosa, Agostinho C.micompm is a MATLAB / GNU Octave port of the original micompr R package for comparing multivariate samples associated with different groups. Its purpose is to determine if the compared samples are significantly different from a statistical point of view. This method uses principal component analysis to convert multivariate observations into a set of linearly uncorrelated statistical measures, which are then compared using statistical tests and score plots. This technique is independent of the distributional properties of samples and automatically selects features that best explain their differences, avoiding manual selection of specific points or summary statistics. The procedure is appropriate for comparing samples of time series, images, spectrometric measures or similar multivariate observations. It is aimed at researchers from all fields of science, although it requires some knowledge on design of experiments, statistical testing and multidimensional data analysis.Item MN-DS: A Multilabeled News Dataset for News Articles Hierarchical Classification(MDPI, 2023-04-23) Petukhova, Alina; Fachada, NunoThis article presents a dataset of 10,917 news articles with hierarchical news categories collected between 1 January 2019 and 31 December 2019. We manually labeled the articles based on a hierarchical taxonomy with 17 first-level and 109 second-level categories. This dataset can be used to train machine learning models for automatically classifying news articles by topic. This dataset can be helpful for researchers working on news structuring, classification, and predicting future events based on released news. Keywords: news dataset; text classification; NLP; media topic taxonomyItem Model-independent comparison of simulation output(Elsevier, 2017) Fachada, Nuno; Lopes, Vitor V.; Martins, Rui C.; Rosa, Agostinho C.Computational models of complex systems are usually elaborate and sensitive to implementation details, characteristics which often affect their verification and validation. Model replication is a possible solution to this issue. It avoids biases associated with the language or toolkit used to develop the original model, not only promoting its verification and validation, but also fostering the credibility of the underlying conceptual model. However, different model implementations must be compared to assess their equivalence. The problem is, given two or more implementations of a stochastic model, how to prove that they display similar behavior? In this paper, we present a model comparison technique, which uses principal component analysis to convert simulation output into a set of linearly uncorrelated statistical measures, analyzable in a consistent, model-independent fashion. It is appropriate for ascertaining distributional equivalence of a model replication with its original implementation. Besides model-independence, this technique has three other desirable properties: a) it automatically selects output features that best explain implementation differences; b) it does not depend on the distributional properties of simulation output; and, c) it simplifies the modelers’ work, as it can be used directly on simulation outputs. The proposed technique is shown to produce similar results to the manual or empirical selection of output features when applied to a well-studied reference model.