Penerapan Model Spiral Pada Rancang Bangun Game Platformer

Di Indonesia, industri game merupakan salah satu sektor ekonomi kreatif yang sangat berkembang. Omzet industri game nasional meningkat pada tahun 2018 hingga US$ 1,13 miliar. Peningkatan jumlah pengguna game di Indonesia tidak sebanding dengan jumlah pengembang local. Hal ini menyebabkan pengguna game di Indonesia masih banyak menikmati game buatan dari luar Indonesia. Konten-konten ataupun jenis permainan dalam game beragam. Unsur pornografipun juga disajikan dalam game. Hal ini menjadi tantangan bagi pengembang lokal untuk menyajikan game yang berkualitas yang mengedepankan norma di Indonesia. Pada paper ini menyajikan perancangan sebuah game platformer 2D sebagai sarana hiburan. Perancangan game tersebut menggunakan metode spiral. Metode ini merupakan metode software proses yang terdiri dari empat tahapan. Empat tahapan tersebut adalah planning, analisa resiko, perancangan dana evaluasi. Proses pembuatan game pada penelitian ini dilakukan dengan dua iterasi. Hasil pengujian menunjukkan bahwa game berhasil dirancang dengan tingkat kepuasan pengguna dari segi usability sebesar 81,5 % dan dari segi functionality sebesar 82,8 %. Game ini juga berhasil menjadi sarana hiburan dengan tingkat kepuasan pengguna sebesar 82,6% dan tingkat imersif sebesar 81,1%

E-polis: A serious game for the gamification of sociological surveys

E-polis is a multi-platform serious game that gamifies a sociological survey for studying young people's opinions regarding their ideal society. The gameplay is based on a user navigating through a digital city, experiencing the changes inflicted, triggered by responses to social and pedagogical surveys, known as "dilemmas". The game integrates elements of adventure, exploration, and simulation. Unity was the selected game engine used for the development of the game, while a middleware component was also developed to gather and process the users' data. At the end of each game, users are presented with a blueprint of the city they navigated to showcase how their choices influenced its development. This motivates them to reflect on their answers and validate them. The game can be used to collect data on a variety of topics, such as social justice, and economic development, or to promote civic engagement and encourage young people to think critically about the world around them.Comment: 8 pages, 11 figures, Proceedings of the International Conference on Applied Mathematics & Computer Science (ICAMCS) 202

Jumping Caveman: A Tool for Manipulating Player Experience and Answering Questions in Games Research

Standard tools exist for assessing player experience; however, there are few tools for inducing play experiences. Game researchers without the resources to operationalize an experimental factor of interest as an implemented mechanic in the design of a custom game are therefore limited in the types of controlled experiments they can conduct. Modifying an existing off-the-shelf game leverages the design and resources of game studio development, but researchers are limited in what type of questions they can ask due to the lack of access control on the source code. We present an open-source system that can be used by game researchers to manipulate player experience in a reliable way and at a finer time resolution than has previously been reported. We simulate the experience of success and failure by providing covert assistance or hindrance to a player, as this has been shown to reliably affect player experience measures. Through three studies, we show that the system manipulates experience in an intended and predictable way. With our system, researchers can also modify the experiment design through simple configuration interface - which allows them to quickly create experiment conditions even if they do not possess technical knowledge of game development. There are many research questions that revolve around the experience of in-game success or failure and our tool allows researchers to ask and answer interesting questions in games research through controlled experiments

Designing Persuasively using Playful Elements

Alongside productivity and communication, computers are a valuable tool for diversion and amusement. Game Designers leverage the multifaceted world of computing to create applications that can be developed persuasively; designs can be formulated to compel users towards actions and behaviours which range from engaging in the game’s mechanics, micro-transactions, or in more complex manifestations such as encouraging reflection via the evaluation of the moral argument presented in the gameplay narrative. In my dissertation, I explore how to create compelling experiences during playful interactions. Particularly, I explore how design decisions affect users’ behaviours, and evaluations of the gaming experience to learn more about crafting persuasive mechanics in games. First, I present research on calibrating aspects of difficulty and character behaviour in the design of simple games to create more immersive experiences. My work on calibration of game difficulty, and enemy behaviour contribute insight regarding the potential of games to create engaging activities, which inspire prolonged play sessions. Further work in my dissertation explores how players interact with in-game entities they perceive as human and explores the boundaries of acceptable player interaction during co-located gaming situations. My early work gives rise to deeper questions regarding perspectives on co-players during gaming experiences. Specifically, I probe the question of how players perceive human versus computer-controlled teammates during a shared gaming experience. Additionally, I explore how game design factors in the context of a tightly-coupled shared multi-touch large display gaming experience can influence the way that people interact and, in turn, their perspectives on one another to ask: ‘how can games be used persuasively to inspire positive behaviours and social interaction?’. Issues of perspectives are a theme I carry forward in my work by exploring how game dynamics – in particular the use of territoriality – can be used to foster collaborative behaviours. Further, I discuss how my work contributes to the study of persuasive game design, games with purpose, and cement my findings in relation to the games studies and computer science literature. Last, I discuss future work, in which I discuss my ambitions for using persuasive design for social good via Games4Change

Ajuste dinâmico de dificuldade híbrido em um jogo do gênero plataforma

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2019.Conforme a indústria de videogames cresce, novos cenários surgem e os jogos devem se manter divertidos para distintos perfis de consumidor, com variados níveis de habilidades e preferências. Assim, surge um campo de pesquisa a partir da percepção e de mecanismos de ajuste da dificuldade nos jogos eletrônicos. Ou seja, um jogo pode ser tedioso quando muito fácil ou frustrante quando muito difícil, precisando oferecer um desafio contínuo e condizente ao jogador para mantê-lo motivado. A implementação de um sistema de dificuldade, ao se adaptar automaticamente à performance do jogador, pode melhorar a experiência geral do jogador com o jogo. Esses sistemas são comumente lineares, seguindo um padrão médio do público almejado. No entanto, a dificuldade pode ser adaptada de acordo com o desempenho do jogador, com seu estado afetivo ou a conjunção de ambos os modelos. No âmbito deste trabalho, objetiva-se investigar um método de estimação da dificuldade de níveis de jogos do gênero plataforma e se um mecanismo híbrido do Ajuste Dinâmico de Dificuldade (ADD) possibilita adequar a dificuldade ao jogador e mantê-lo em estado de fluxo, além de comparar sua eficiência com os outros modelos. Para isso, um jogo foi desenvolvido para se adaptar com base aos dados extraídos por algoritmos de análise de desempenho do jogador correlacionados aos obtidos por um sensor de captura de dados fisiológicos, mais especificamente pela Atividade Eletrodérmica (EDA). Além de jogar com os distintos modelos de ADD, cada participante respondeu questionários e teve seus dados coletados para averiguação dos objetos de pesquisa. O modelo híbrido demonstrou-se capaz de manter o jogador em estado de fluxo e adequar a dificuldade ao jogador, com resultados superiores aos demais modelos.As the video game industry grows, new scenarios arise and games should be entertaining for different consumer profiles with varying skill levels and preferences. Thus, a field of research emerges from the perception and mechanisms of adjustment of the difficulty in electronic games. That is, a game can be tedious when very easy or frustrating when very difficult, needing to offer a continuous and appropriate challenge to the player to keep him motivated. The implementation of a difficulty system, when adapting automatically to the performance of the player, can improve the overall experience of the player in the game. These systems are commonly linear, following the average pattern of the target audience. However, the difficulty can be adapted according to the player’s performance, his affective state or the conjunction of both models. In the scope of this work, the objective is to investigate a method that estimates the difficulty of game levels of the platform genre and if a hybrid Dynamic Difficulty Adjustment (DDA) mechanism makes it possible to adapt the difficulty to the player and keep him in a state of flow, besides comparing its efficiency with the other models. For this, a game was developed to adapt based on the data extracted by analysis algorithms of the player’s performance correlated to those obtained by a sensor that captures physiological data, more specifically by the Electrodermal Activity (EDA). In addition to playing with the different DDA models, each participant answered questionnaires and had their data collected for inquiry purposes. The hybrid model was able to keep the player in a state of flow and to adapt the difficulty to the player, with superior results compared to other models

Ajuste Dinâmico de Dificuldade pelo desempenho e perfil de jogador em jogo de plataforma

Dissertação (Mestrado em Informática) — Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, Brasília, 2022.O Ajuste Dinâmico de Dificuldade (ADD) dos jogos pode desempenhar um papel importante para aumentar o envolvimento e a diversão do jogador. A dificuldade de jogo pode ser adaptada de acordo com o desempenho do jogador, seu estado afetivo ou usando um modelo híbrido que combina as duas abordagens. Ademais, pode adaptar configurações ou componentes do jogo e utilizar métricas pré-estabelecidas ou aprendizado de máquina para análise do que será adaptado. Este trabalho investiga os distintos mecanismos de um sistema de ADD para um jogo de plataforma adaptar adequadamente seu nível de dificuldade e manter o jogador em um estado de fluxo. Este trabalho contribui com a definição de um método que estima a dificuldade do jogo a partir de características específicas de componentes comuns ao gênero de plataforma. Também são revisadas métricas para medição do estado do fluxo e do perfil do jogador e propostas regras para criação de níveis ao testar modelos de ADD. O ajuste proposto varia o tamanho da plataforma e a altura do salto, comparando distintas abordagens a partir de sistemas do jogo e verificando a eficiência de cada uma em relação ao monitoramento e análise dos dados e ao controle da adaptação dos componentes. Um jogo de plataforma de código aberto foi adaptado para suportar os algoritmos de ADD e para execução de testes com grupos de amostra, nos quais os participantes respondiam a questionários e tiveram seus dados coletados para fins de investigação. Os resultados indicaram que a dificuldade de jogos de plataforma pode ser estimada pelos componentes dos níveis, incluindo correlação entre a dificuldade e os dados de desempenho dos jogadores. Além disso, perfis de jogadores foram previstos a partir de dados brutos da sessão do jogo e utilizados com métodos de aprendizado de máquina para definir a progressão de dificuldade. Por fim, os modelos de ADD foram capazes de ajustar a dificuldade do jogo aos jogadores, diminuindo a dispersão entre os dados de desempenho e mantendo o jogador em estado de fluxo, especialmente ao utilizar redes neurais diretas para predição da dificuldade experienciada e do perfil do jogador.The Dynamic Difficulty Adjustment (DDA) of games can play an important role in increasing the player engagement and fun. Gameplay difficulty can be adapted according to the player’s performance, its affective state or by using a hybrid model that combines both approaches. In addition, you can adapt game settings or components and use pre-established metrics or machine learning to analyze what will be adapted. This work investigates the different mechanisms of an DDA system for a platform game to adequately adapt its difficulty level and keep the player in a state of flow. This work contributes with the definition of a method that estimates the game’s difficulty based on specific characteristics of components common to the platform genre. Metrics for measuring the flow state and player profile are also reviewed, and rules for creating levels when testing ADD models are proposed. The proposed adjustment varies the size of the platform and the height of the jump, comparing different approaches from the game systems and verifying the efficiency of each one in relation to the monitoring and analysis of the data and the control of the components adaptation. An open source platform game was adapted to support the ADD algorithms and to run tests with sample groups, in which participants answered questionnaires and had their data collected for research purposes. The results indicated that the difficulty of platform games can be estimated by the components of the levels, including correlation between the difficulty and player performance data. In addition, player profiles were predicted from raw game session data and used with machine learning methods to define difficulty progression. Finally, the DDA models were able to adjust the game difficulty to the players, decreasing the dispersion between the performance data and keeping the player in a state of flow, especially when using feedforward neural networks to predict the difficulty experienced and the player’s profile