Towards the Identification of Players’ Profiles Using Game’s Data Analysis Based on Regression Model and Clustering

Personalization of serious games is an important factor for motivating and engaging players. It requires the identification of players’ profiles through the analysis of large volume of data including game data. This research study aims at identifying relevant data from an online serious game and the appropriate data mining methods for deduction of players’ profiles. Multiple linear regression is applied to analyze the influence of player’s characteristics on his performance. Moreover, clustering technique is used, in particular K-means, to extract players’ clusters and to identify their common characteristics. The regression models showed that the number of access to the game, completed quests and advantages used contribute significantly to the scores and the gaming duration, while the clustering revealed three forms of players’ participation: beginner, intermediate and advanced; who interact with the game according to their experiences

Knowledge extraction from the behaviour of players in a web browser game

Dissertação de mestrado em Informatics EngineeringThe analysis of the player’s behaviour is a requirement with growing popularity in the traditional computer games segment and has been proven to aid the developers create better and more profitable games. There is now interest in trying to replicate this attainment in a less conventional genre of games known as web browser games. The main objective of this work is to analyse and create a technique for the analysis of the behaviour of the players inside a web browser game. For this analysis a system to automatically collect, process and store the relevant data for the referred analysis was developed. The web browser game used as a case study for this work is developed by 5DLab and is called Wack-a- Doo. The work developed focused on creating short-term prediction models using the information collected during the first days of playing for each player. The objectives of these models are to predict the time played or the conversion state of the players. With the study of the created models it was possible to extract results that provide potentially useful information to increase the profitability of Wack-a-Doo.A análise do comportamento de jogadores é uma prática com crescente popularidade no segmento dos jogos de vídeo tradicionais. Esta técnica foi já aprovada como capaz de ajudar os criadores a desenvolver melhores e mais lucrativos jogos. Existe agora interesse em tentar replicar este sucesso num género de jogos de vídeo menos convencionais normalmente referidos como jogos de browser web. O objetivo deste trabalho é analisar e criar uma técnica para essa análise do comportamento dos jogadores de um jogo de browser web. Para isto um sistema automático de recolha, processamento e armazenamento dos dados relacionados com o comportamento dos jogadores foi desenvolvido. O jogo de browser web usado para este estudo foi criado pela empresa 5DLab e dá pelo nome de Wack-a-Doo. O trabalho desenvolvido centrou-se em fazer modelos de previsão de curto prazo usando as informações recolhidas durante os primeiros dias de jogo de cada jogador. Estes modelos têm como objetivo prever o tempo jogado e o estado de conversão do jogador. Estudando os modelos criados foi possível extrair resultados que fornecem informação potencialmente útil para melhorar a rentabilidade do Wack-a-Doo

Mimicking human player strategies in fighting games using game artificial intelligence techniques

Fighting videogames (also known as fighting games) are ever growing in popularity and accessibility. The isolated console experiences of 20th century gaming has been replaced by online gaming services that allow gamers to play from almost anywhere in the world with one another. This gives rise to competitive gaming on a global scale enabling them to experience fresh play styles and challenges by playing someone new. Fighting games can typically be played either as a single player experience, or against another human player, whether it is via a network or a traditional multiplayer experience. However, there are two issues with these approaches. First, the single player offering in many fighting games is regarded as being simplistic in design, making the moves by the computer predictable. Secondly, while playing against other human players can be more varied and challenging, this may not always be achievable due to the logistics involved in setting up such a bout. Game Artificial Intelligence could provide a solution to both of these issues, allowing a human player s strategy to be learned and then mimicked by the AI fighter. In this thesis, game AI techniques have been researched to provide a means of mimicking human player strategies in strategic fighting games with multiple parameters. Various techniques and their current usages are surveyed, informing the design of two separate solutions to this problem. The first solution relies solely on leveraging k nearest neighbour classification to identify which move should be executed based on the in-game parameters, resulting in decisions being made at the operational level and being fed from the bottom-up to the strategic level. The second solution utilises a number of existing Artificial Intelligence techniques, including data driven finite state machines, hierarchical clustering and k nearest neighbour classification, in an architecture that makes decisions at the strategic level and feeds them from the top-down to the operational level, resulting in the execution of moves. This design is underpinned by a novel algorithm to aid the mimicking process, which is used to identify patterns and strategies within data collated during bouts between two human players. Both solutions are evaluated quantitatively and qualitatively. A conclusion summarising the findings, as well as future work, is provided. The conclusions highlight the fact that both solutions are proficient in mimicking human strategies, but each has its own strengths depending on the type of strategy played out by the human. More structured, methodical strategies are better mimicked by the data driven finite state machine hybrid architecture, whereas the k nearest neighbour approach is better suited to tactical approaches, or even random button bashing that does not always conform to a pre-defined strategy

Analise de técnicas de clusterização em MMO com dados restritos : o caso de Final Fantasy XIV

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2019.A utilização de dados de uma sessão de jogo para melhor compreensão do comportamento do jogador e os possíveis melhoramentos que podem ser realizados é um dos objetos de estudo da Game Analytics, domínio de pesquisa multidisciplinar que vem se difundindo amplamente entre pesquisadores da área de jogos eletrônicos. Entretanto, no caso dos MMOGs (Massive Multiplayer Online Games), os tipos de dados disponibilizados para análise não são padronizados, usualmente variando de um jogo para outro. Assim, um dos desafios desta área consiste em determinar o tipo de informação que pode ser obtida de um jogo MMO específico, assim como qual técnica de mineração de dados utilizar ou desenvolver em função da especificidade de sua base de dados. O objetivo deste trabalho é o estudo de técnicas de clusterização aplicadas ao Final Fantasy XIV, jogo que conta com uma base de milhões de jogadores mas disponibiliza apenas uma limitada quantidade de dados para análise e, portanto, tem sido pouco estudado na literatura. Os resultados obtidos poderão contribuir para uma melhor compreensão sobre os grupos de jogadores contidos em Final Fantasy XIV e fornecer uma base para o desenvolvimento de trabalhos futuros, além de prover um estudo de caso sobre técnicas de clusterização aplicadas sobre um limitado conjunto de dados de jogo.CAPESUsing data from a game session to better understand player behavior and possible improvements that can be made in a game is one of the objects of study at Game Analytics, a multidisciplinary research domain that has been widely spread among researchers in the field of electronic games. However, for Massive Multiplayer Online Games (MMOGs), the types of data available for analysis are not standardized, usually varying from game to game. Thus, one of the challenges in this area is to determine the type of information that can be obtained from a specific MMO game, as well as which data mining technique to use or develop depending on the specificity of its database. The aim of this paper is the study of clustering techniques applied to Final Fantasy XIV, a game that has a player base of millions but provides only a limited amount of game data for analysis and, therefore, has been little studied in the literature. The results obtained may contribute to a better understanding of the Final Fantasy XIV player groups and provide a basis for future work, as well as provide a case study on clustering techniques applied over a limited set of game data

Towards the correlation of player preferences and behaviour for video game personalisation

As an electronic medium, video games are capable of adapting its rules and content to individual players at run-time in ways defined by designers during development. This player-centric video game adaptation is what we mean by video game personalisation. However, to enable a personalisation system to adapt a video game without explicitly asking the player each time, the difficult task of predicting some relevant aspect of the player becomes necessary. This thesis describes a methodology for observing a player profile made up of 22 gameplay preferences and player behaviour data within a testbed role-playing video game. Our primary goal was to test whether specific preferences and behavioural trends correlate in order to permit the prediction of gameplay preferences from in-game behaviour. A successful finding would enable video game designers to define gameplay rules that are dependent on the preferences of their future players, thus providing one avenue for the future commercial adoption of video game personalisation. While our results were inconclusive, the rationale for the process we followed is carefully described and contains many important considerations for future research of a similar type. It is still our firm belief that other work can build upon our own to one day enable some form of video game personalisation