Player experience and deceptive expectations of difficulty adaptation in digital games

Increasingly, digital games are including adaptive features that adjust the level of difficulty to match the skills of individual players. The intention is to improve and prolong the player experience by allowing the player to have the feeling of challenge without it being overwhelming and leading to repeated failure and frustration. Previous work has shown that player experience is indeed improved by such adaptations but also that the player experience can be improved by simply claiming such an adaptation is present even when it is not. It is therefore possible that claims about adaptations and the actual adaptations could interact and not lead to the intended outcomes for the players or worse disappoint players. This paper reports on two studies that were conducted to experimentally investigate the interaction between game adaptations and player information about adaptations on the player experience, specifically their sense of immersion in the game. For this, two games were developed using two different kinds of adaptations to adjust difficulty based on players’ performance in the game. Participants were provided with information about game adaptations independently of whether the adaptations were present. The results suggest that players felt more immersed in the game when told that the game adapts to them, regardless of whether the adaptation was present in the game or not. This effect was observed in both games despite their different adaptations and it remained prominent even during longer gaming sessions. These findings demonstrate that players’ knowledge of adaptations influences their experience independently of adaptations. In this particular context, the knowledge reinforced the experience of the adaptations. This suggests that, at least in some circumstances, developers do not need to be concerned about negative effects of telling players about in-game adaptations

How Fast Can We Play Tetris Greedily With Rectangular Pieces?

Consider a variant of Tetris played on a board of width $w$ and infinite height, where the pieces are axis-aligned rectangles of arbitrary integer dimensions, the pieces can only be moved before letting them drop, and a row does not disappear once it is full. Suppose we want to follow a greedy strategy: let each rectangle fall where it will end up the lowest given the current state of the board. To do so, we want a data structure which can always suggest a greedy move. In other words, we want a data structure which maintains a set of $O(n)$ rectangles, supports queries which return where to drop the rectangle, and updates which insert a rectangle dropped at a certain position and return the height of the highest point in the updated set of rectangles. We show via a reduction to the Multiphase problem [P\u{a}tra\c{s}cu, 2010] that on a board of width $w=\Theta(n)$, if the OMv conjecture [Henzinger et al., 2015] is true, then both operations cannot be supported in time $O(n^{1/2-\epsilon})$ simultaneously. The reduction also implies polynomial bounds from the 3-SUM conjecture and the APSP conjecture. On the other hand, we show that there is a data structure supporting both operations in $O(n^{1/2}\log^{3/2}n)$ time on boards of width $n^{O(1)}$, matching the lower bound up to a $n^{o(1)}$ factor.Comment: Correction of typos and other minor correction

Comparação de ajustes dinâmicos de dificuldade aplicados a diferentes elementos de jogo

Trabalho de conclusão de curso (graduação)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2021.Conforme a indústria de videogames cresce, há o surgimento de novos cenários, sendo essencial manter os jogos divertidos para distintos perfis de consumidor, os quais podem possuir diversos níveis de habilidades e preferências. Nesse contexto é relevante a imple- mentação de um sistema de dificuldade adaptável ao jogador, assim oferecendo um desafio condizente ao jogador, evitando que o jogo se torne tedioso por estar muito fácil ou muito difícil. Diversos sistemas já foram modelados para lidar com essa questão, dentre eles alguns específicos ao gênero de jogo plataforma. No âmbito deste trabalho, objetiva-se comparar a eficiência de um sistema de ajuste dinâmico de dificuldade (ADD) por desem- penho aplicado a elementos diferentes de um jogo de plataforma. Foram comparados um total de quatro casos: sem aplicação de ADD, aplicação de ADD às plataformas, aplicação de ADD ao pulo e aplicação de ADD a ambos. Para tanto, utilizou-se uma evolução do trabalho de Rosa [1], sobre o qual foram implementadas as devidas modificações e para a condução de testes remotos cujo os dados foram coletados através de um Google Forms. A análise desses dados revelou conclusões multifacetadas, com o caso que melhor permite o estado de fluxo sendo o caso de ADD combinado, seguido pelo ADD de plataforma.As the video game industry grows, new scenarios are emerging, so it is essential to keep games fun for different consumer profiles, who may have varying levels of skills and pref- erences. In this context, it is relevant to implement a difficulty system that adapts to the player, thus offering a suitable challenge to the player, preventing the game from becoming tedious because it is too easy or too difficult. Several systems have already been modeled to deal with this issue, among them some specific to the platform game genre. In the scope of this work, the objective is to compare a system of dynamic difficulty adjustment (DDA) by performance applied to different elements of a platform game, to find out which one has the best efficiency in keeping the player in the flow state. A total of four cases were compared: without application of ADD, application of ADD to platforms, applica- tion of ADD to the jump and application of ADD to both. To this end, an evolution of the work of Rosa [1] was used, on which the necessary modifications were implemented and for conducting remote tests whose data were collected through a Google Forms. The analysis of these data revealed multifaceted conclusions, with the case that best allows the flow state being the case of combined ADD, followed by the platform ADD

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

Personalized Game Content Generation and Recommendation for Gamified Systems

Gamification, that is, the usage of game content in non-game contexts, has been successfully employed in several application domains to foster engagement, as well as to influence the behavior of end users. Although gamification is often effective in inducing behavioral changes in citizens, the difficulty in retaining players and sustaining the acquired behavior over time, shows some limitations of this technology. That is especially unfortunate, because changing players’ demeanor (which have been shaped for a long time), cannot be immediately internalized; rather, the gamification incentive must be reinforced to lead to stabilization. This issue could be sourced from utilizing static game content and a one-size-fits-all strategy in generating the content during the game. This reveals the need for dynamic personalization over the course of the game. Our research hypothesis is that we can overcome these limitations with Procedural Content Generation (PCG) of playable units that appeal to each individual player and make her user experience more varied and compelling. In this thesis, we propose a deep, large and long solution, deployed in two main phases of Design and Integration to tackle these limitations. To support the former phase, we present a “PCG and Recommender system” to automate the generation and recommendation of playable units, named “Challenges”, which are Personalized and Contextualized on the basis of players’ preferences, skills, etc., and the game ulterior objectives. To this end, we develop a multi-layered framework to generate the personalized game content to be assigned and recommended to the players involved in the gamified system. To support the latter phase, we integrate two modules into the system including Machine Learning (ML) and Player Modeling, in order to optimize the challenge selection process and learning players’ behavior to further improve the personalization, by deriving the style of the player, respectively. We have carried out the implementation and evaluation of the proposed framework and its integration in two different contexts. First, we assess our Automatic Procedural Content Generation and Recommendation (APCGR) system within a large-scale and long-running open field experiment promoting sustainable urban mobility that lasted twelve weeks and involved more than 400 active players. Then, we implement the “Player Modeling” module (in the integration phase) in an educational interactive game domain to assess the performance of the proposed play style extraction approach. The contributions of this dissertation are a first step toward the application of machine learning in automating the procedural content generation and recommendation in gamification systems

Una metodología de ajuste dinámico de dificultad en videojuegos : entre Rubber Band AI y la teoría de flow

Uno de los desafíos más relevantes durante la producción de videojuegos es la creación de retos adecuados para los jugadores. Normalmente, la producción de un videojuego AAA toma aproximadamente 2 años y la razón principal es que las ideas que se quieren desarrollar deben ser probadas, no solo para encontrar errores en el código, sino también para validar que sean entretenidas y factibles. Este trabajo tiene como objetivo crear una metodología que facilite el establecimiento de retos adecuados empleando ajuste dinámico de dificultad en videojuegos. Después de revisar la literatura sobre el modelado de jugadores y el ajuste dinámico de dificultad, realizamos varios experimentos donde los participantes jugaron diversas versiones de Tetris. La versión que implementaba la metodología propuesta predecía el nivel de habilidad del jugador actual extrayendo los datos de sus últimas acciones y comparaba la evolución de la partida con partidas de otros jugadores. Luego, decidía si se modificaba la dificultad del juego teniendo en cuenta el nivel de habilidad previamente calculado. Usamos dos enfoques de ajuste dinámico de dificultad, el Ruber Band AI y la teoría del flow, para implementar las diversas versiones de Tetris. Además, los participantes respondieron cuestionarios con el fin de conocer qué tan satisfactoria fue su experiencia en cada sesión..