Percorrer por autor "Lopes, Phil"
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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 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 Modelling physiological sensor noise to movement-based virtual reality activities(Scitepress, 2024-02-23) Lopes, Phil; Fachada, Nuno; Fonseca, Micaela; Gamboa, Hugo; Quaresma, ClaudiaThis position paper proposes the hypothesis that physiological noise artefacts can be classified based on the type of movements performed by participants in Virtual Reality contexts. To assess this hypothesis, a detailed research plan is proposed to study the influence of movement on the quality of the captured physiological signals. This paper argues that the proposed plan can produce a valid model for classifying noisy physiological signal features, providing insights into the influence of movement on artefacts, while contributing to the development of movement-based filters and the implementation of best practices for using various associated technologies.