Playing an educational game featuring procedural content generation: which attributes impact players’ curiosity?

Understanding which and how attributes impact player experience can contribute to designing more tailored tools, providing concerns on how to improve these. However, there is a gap in the understanding of what impacts learners’ experience when interacting with Educational Games (EG) featuring Procedural Content generation (PCG) as these have been scantly used together. This article presents an empirical study on which attributes impact both math’s and game’s curiosity of players when interacting with an EG that uses PCG. The results show the attributes that led to higher or lower curiosity, as well aswhich of them are associated with it. Hence, advancing the understanding of what drives players’ curiosity, contributing to the design of EG that feature PCG

Node-Based Native Solution to Procedural Game Level Generation

A Geração Procedural de Conteúdo (PCG) aplicada ao domínio do desenvolvimento de jogos tem se tornado um tópico proeminente, com um número crescente de implementações e aplicações. Soluções de PCG standalone e plugin, regidas por interfaces baseadas em nós e outros modelos de alto nível, enfrentam limitações em termos de integração, interatividade e responsividade quando inseridas no processo de desenvolvimento de jogos. Essas limitações afetam a experiência do utilizador e inibem o verdadeiro potencial que estes sistemas podem oferecer. Adotando uma metodologia de Action-Research, realizou-se um estudo preliminar com entrevistas a especialistas da área. A avaliação da pertinência desta metodologia nativa e da abordagem visual mais adequada para a sua interface foi efetuada através de uma série de protótipos. Posteriormente, foi implementado um protótipo funcional e conduzido um estudo de caso com uma amostra constituída por um grupo de especialistas em PCG e de desenvolvedores de jogos. Os participantes realizaram uma série de exercícios que estavam documentados com os respetivos tutoriais. Após a conclusão dos exercícios propostos, os participantes avaliaram a relevância da solução e da experiência do utilizador através de um questionário. No desenvolvimento de uma metodologia nativa de PCG baseado em nós, integrado no motor de jogo, identificamos limitações e concluímos que existem diversos desafios ainda por superar no que diz respeito a uma implementação completa de um sistema complexo e amplo.Procedural Content Generation (PCG) applied to game development has become a prominent topic with increasing implementations and use cases. However, existing standalone and plugin PCG solutions, which use Node-based interfaces and other high-level approaches, face limitations in integration, interactivity, and responsiveness within the game development pipeline. These limitations hinder the overall user experience and restrain the true potential of PCG systems. Adopting an Action-Research methodology, a preliminary interview was conducted with experts in the field. The relevance assessment of this native methodology and the most suitable visual approach for its interface was carried out through a series of prototypes. Subsequently, a functional prototype was implemented, and a case study was conducted using a sample consisting of a group of PCG experts and game developers. The participants performed a series of exercises documented with the respective tutorials. After completing the exercises, the solution's relevancy and user experience were evaluated through a questionnaire. In developing a native node-based PCG methodology integrated into the game engine, we identified limitations. We concluded that several challenges are yet to be overcome regarding fully implementing a complex and extensive system

Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence