Percorrer por autor "Lopes, Phil"

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    Active learning prototypes for teaching game AI
    (IEEE Computer Society, 2023) Fachada, Nuno; Barreiros, Filipa F.; Lopes, Phil; Fonseca, Micaela; COPELABS - Cognitive and People-centric Computing; HEI-LAB - Human Environment Interaction Lab
    Artificial 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.
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    Closing the loop : a systematic review of experience-driven game adaptation
    (2025-05-02) Lopes, Phil; Fachada, Nuno; Fonseca, Maria; ECATI - School of Communication, Architecture, Arts and Information Technologies; HEI-LAB - Human Environment Interaction Lab; COPELABS - Cognitive and People-centric Computing
    Adaptive game systems aim to enrich player experiences by dynamically adjusting game content in response to user data. While extensive research has addressed content personalization and player experience modeling, the integration of these components into fully operational adaptive gameplay systems remains limited. This systematic review, conducted in accordance with PRISMA guidelines, analyzes 17 empirical studies published between January 2015 and May 2024, identifying and analyzing approaches that implement the complete experience-driven loop -- including player sensing, modeling, and content adaptation. Game telemetry remains the most prevalent sensing modality, although other non-invasive methods suitable for affective modeling -- such as facial expression analysis (FEA) and peripheral interaction data -- remain underutilized despite their potential for real-time emotional inference. Knowledge-based methods, such as rule-based systems and heuristics, dominate modeling and adaptation due to their interpretability and low resource demands, whereas machine learning approaches face challenges related to data availability and transparency. Despite their relevance to immersive and therapeutic experiences, affective states such as stress and anxiety remain largely ignored, as systems continue to favor performance over emotion-sensitive adaptation. These findings highlight a crucial research direction: advancing emotionally responsive game systems that move beyond performance optimization by incorporating underutilized sensing modalities -- such as FEA and peripheral interaction -- to enable real-time affect-driven personalization. Advancing in this direction holds strong potential to increase immersion, personalize gameplay, and support affect regulation across entertainment and therapeutic contexts.
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    Games! What are they good for? : the struggle of serious game adoption for rehabilitation
    (2025-01-12) Fonseca, Maria Micaela; Fachada, Nuno; Sousa, Micael; Oliveira, Jorge; Rodrigues, Pedro; Sousa, Sara; Quaresma, Claudia; Lopes, Phil; HEI-LAB - Human Environment Interaction Lab; ECATI - School of Communication, Architecture, Arts and Information Technologies; COPELABS - Cognitive and People-centric Computing
    The field of serious games for health has grown significantly, demonstrating effectiveness in various clinical contexts such as stroke, spinal cord injury, and degenerative neurological diseases. Despite their potential benefits, therapists face barriers to adopting serious games in rehabilitation, including limited training and game literacy, concerns about cost and equipment availability, and a lack of evidence-based research on game effectiveness. Serious games for rehabilitation often involve repetitive exercises, which can be tedious and reduce motivation for continued rehabilitation, treating clients as passive recipients of clinical outcomes rather than players. This study identifies gaps and provides essential insights for advancing serious games in rehabilitation, aiming to enhance their engagement for clients and effectiveness as a therapeutic tool. Addressing these challenges requires a paradigm shift towards developing and co-creating serious games for rehabilitation with therapists, researchers, and stakeholders. Furthermore, future research is crucial to advance the development of serious games, ensuring they adhere to evidence-based principles and engage both clients and therapists. This endeavor will identify gaps in the field, inspire new directions, and support the creation of practical guidelines for serious games research.
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    Generating 3D Terrain with 2D Cellular Automata
    (2024-06-01) Fachada, Nuno; Rodrigues, António R.; de Andrade, Diogo; Lopes, Phil; COPELABS - Cognitive and People-centric Computing; HEI-LAB - Human Environment Interaction Lab
    This paper presents an initial exploration on the use of 2D cellular automata (CA) for generating 3D terrains through a simple yet effective additive approach. By experimenting with multiple CA transition rules, this preliminary investigation yielded aesthetically interesting landscapes, hinting at the technique's potential applicability for real-time terrain generation in games.