対戦ログに基づいた多様な戦略を持つポーカーAIの構築

コンピュータゲームの普及により、勝つことだけを目的にするのではなく、人間プレイヤーを楽しませるためのゲームAIの需要が高まっている。これらのAIにはプレイヤーを飽きさせないための性格付けや、違和感を感じさせない人間らしさが求められる。AIの持つパラメータの調整を自動化することで、AIを生成する手法は従来から研究されている。しかしAIパラメータとして利用する特徴量の選択に人の知識を必要とする点や、AIの行動アルゴリズムの設計の困難さ、人間プレイヤーに違和感を感じさせない範囲でパラメータ調整を行う困難さなどいくつかの課題がある。本研究ではゲームにおけるプレイヤーのログから類似の戦略を分類するとともに、各クラスタを代表する戦略を学習する手法を提案する。提案手法ではニューラルネットワークを使用することで、特徴量抽出や行動アルゴリズムの設計を必要としないAIの生成が可能である。また、対戦ログに基づいて戦略を学習することで、人間プレイヤーから乖離した戦略が生成されるのを防止することができる。提案手法を評価するためテキサスホールデムポーカーにおいて、人の知識に基づくルールベースのエージェントと、パラメータを持つルールベースエージェントそれぞれから生成された対戦ログを用いて実験を行った。どちらの実験においても提案手法は対戦ログから類似する戦略を分類できることを確認した。また、パラメータを持つエージェントを用いた実験では、学習された戦略がエージェントの特性の一部を再現することが示された。電気通信大学201

Forecasting Player Behavioral Data and Simulating in-Game Events

Understanding player behavior is fundamental in game data science. Video games evolve as players interact with the game, so being able to foresee player experience would help to ensure a successful game development. In particular, game developers need to evaluate beforehand the impact of in-game events. Simulation optimization of these events is crucial to increase player engagement and maximize monetization. We present an experimental analysis of several methods to forecast game-related variables, with two main aims: to obtain accurate predictions of in-app purchases and playtime in an operational production environment, and to perform simulations of in-game events in order to maximize sales and playtime. Our ultimate purpose is to take a step towards the data-driven development of games. The results suggest that, even though the performance of traditional approaches such as ARIMA is still better, the outcomes of state-of-the-art techniques like deep learning are promising. Deep learning comes up as a well-suited general model that could be used to forecast a variety of time series with different dynamic behaviors

Data visualization of virtual reality library user data

Abstract. User research is an important part of developing software. In the gaming industry, different ways to analyse user behaviour is an increasingly important part of research. However, as game analytics are relatively new to the game industry, there is limited amount of research available. In this work, we discuss how to visualise collected data in virtual reality environments in a meaningful way to improve product quality and extract user behaviour patterns. We use clustering algorithms and analytical functions to have a more comprehensive look on test participants’ behaviour with our Data Visualization tool. This behaviour is then presented using different path maps, heat maps and data charts. Originally our aim was to conclude research on user behaviour in the Oulu Virtual Library application, but due to the COVID-19 pandemic, we had to change our focus from user research to designing and implementing a tool for researchers to analyse similar data sets as our example data. Even though we had no concrete user data, researchers can use the tool we developed with relative small modifications, when dealing with similar data cases in the future. Usability improvements and real-word experiences are still needed to make the tool more robust.Tiivistelmä. Käyttäjätutkimus on tärkeä osa ohjelmistokehitystä. Koska pelianalytiikka on suhteellisen uutta peliteollisuudessa ja saatavilla oleva tutkimus vähäistä, loppukäyttäjien toiminnan analysointi on yhä tärkeämpi osa peliteollisuuden kehitystä. Tässä tutkielmassa pohditaan, kuinka virtuaaliympäristöistä kerättyä dataa voidaan esittää, merkityksellisellä tavalla, tuotteiden kehittämiseksi ja käyttäjien erilaisten käyttäytymismallien tunnistamiseksi. Käytämme ryhmittelyalgoritmeja ja analyyttisia funktioita, jotta saamme esitettyä käyttäjien toimintaa datavisualisointityökaluamme hyödyntämällä. Käyttäjien toiminta esitetään erilaisten polku- ja lämpökarttojen sekä datakaavioiden avulla. Alkuperäisenä tarkoituksenamme oli tutkia käyttäjien toimintaa Oulun Virtuaalikirjasto-sovelluksessa, mutta COVID-19-pandemian takia jouduimme siirtämään painopisteen käyttäjätutkimuksesta tutkijoille suunnatun datavisualisointityökalun suunnitteluun ja kehitykseen. Vaikka emme saaneet konkreettista aineistoa, tutkijat voivat käyttää työkalua, suhteellisen pienillä muunnoksilla, esimerkkiaineistoa vastaavan aineiston käsitelyyn ja analysointiin tulevaisuudessa. Työkalu tarvitsee yhä käytettävyysparannuksia ja todellisia käyttökokemuksia työkalun käyttövarmuuden parantamiseksi

Method for the player profiling in the turn-based computer games

The following paper presents the players profiling methodology applied to the turn-based computer game in the audience-driven system. The general scope are mobile games where the players compete against each other and are able to tackle challenges presented by the game engine. As the aim of the game producer is to make the gameplay as attractive as possible, the players should be paired in a way that makes their duel the most exciting. This requires the proper player profiling based on their previous games. The paper presents the general structure of the system, the method for extracting information about each duel and storing them in the data vector form and the method for classifying different players through the clustering or predefined category assignment. The obtained results show the applied method is suitable for the simulated data of the gameplay model and clustering of players may be used to effectively group them and pair for the duels

Method for the player profiling in the turn-based computer games

The following paper presents the players profiling methodology applied to the turn-based computer game in the audience-driven system. The general scope are mobile games where the players compete against each other and are able to tackle challenges presented by the game engine. As the aim of the game producer is to make the gameplay as attractive as possible, the players should be paired in a way that makes their duel the most exciting. This requires the proper player profiling based on their previous games. The paper presents the general structure of the system, the method for extracting information about each duel and storing them in the data vector form and the method for classifying different players through the clustering or predefined category assignment. The obtained results show the applied method is suitable for the simulated data of the gameplay model and clustering of players may be used to effectively group them and pair for the duels

Your Gameplay Says It All: Modelling Motivation in Tom Clancy's The Division

Is it possible to predict the motivation of players just by observing their gameplay data? Even if so, how should we measure motivation in the first place? To address the above questions, on the one end, we collect a large dataset of gameplay data from players of the popular game Tom Clancy's The Division. On the other end, we ask them to report their levels of competence, autonomy, relatedness and presence using the Ubisoft Perceived Experience Questionnaire. After processing the survey responses in an ordinal fashion we employ preference learning methods based on support vector machines to infer the mapping between gameplay and the reported four motivation factors. Our key findings suggest that gameplay features are strong predictors of player motivation as the best obtained models reach accuracies of near certainty, from 92% up to 94% on unseen players.Comment: Version accepted for IEEE Conference on Games, 201

k is the Magic Number -- Inferring the Number of Clusters Through Nonparametric Concentration Inequalities

Most convex and nonconvex clustering algorithms come with one crucial parameter: the $k$ in $k$-means. To this day, there is not one generally accepted way to accurately determine this parameter. Popular methods are simple yet theoretically unfounded, such as searching for an elbow in the curve of a given cost measure. In contrast, statistically founded methods often make strict assumptions over the data distribution or come with their own optimization scheme for the clustering objective. This limits either the set of applicable datasets or clustering algorithms. In this paper, we strive to determine the number of clusters by answering a simple question: given two clusters, is it likely that they jointly stem from a single distribution? To this end, we propose a bound on the probability that two clusters originate from the distribution of the unified cluster, specified only by the sample mean and variance. Our method is applicable as a simple wrapper to the result of any clustering method minimizing the objective of $k$-means, which includes Gaussian mixtures and Spectral Clustering. We focus in our experimental evaluation on an application for nonconvex clustering and demonstrate the suitability of our theoretical results. Our \textsc{SpecialK} clustering algorithm automatically determines the appropriate value for $k$, without requiring any data transformation or projection, and without assumptions on the data distribution. Additionally, it is capable to decide that the data consists of only a single cluster, which many existing algorithms cannot