2,380 research outputs found

    Finding Game Levels with the Right Difficulty in a Few Trials through Intelligent Trial-and-Error

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    Methods for dynamic difficulty adjustment allow games to be tailored to particular players to maximize their engagement. However, current methods often only modify a limited set of game features such as the difficulty of the opponents, or the availability of resources. Other approaches, such as experience-driven Procedural Content Generation (PCG), can generate complete levels with desired properties such as levels that are neither too hard nor too easy, but require many iterations. This paper presents a method that can generate and search for complete levels with a specific target difficulty in only a few trials. This advance is enabled by through an Intelligent Trial-and-Error algorithm, originally developed to allow robots to adapt quickly. Our algorithm first creates a large variety of different levels that vary across predefined dimensions such as leniency or map coverage. The performance of an AI playing agent on these maps gives a proxy for how difficult the level would be for another AI agent (e.g. one that employs Monte Carlo Tree Search instead of Greedy Tree Search); using this information, a Bayesian Optimization procedure is deployed, updating the difficulty of the prior map to reflect the ability of the agent. The approach can reliably find levels with a specific target difficulty for a variety of planning agents in only a few trials, while maintaining an understanding of their skill landscape.Comment: To be presented in the Conference on Games 202

    Dynamic Difficulty Adjustment Berbasis Logika Fuzzy Untuk Procedural Content Generation Pada Permainan Roguelike

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    Perkembangan industri video game sangatlah pesat hingga ada banyak sekali orang yang memainkan video game. Setiap orang memiliki tingkat keterampilan yang berbeda dan memiliki kurva belajar yang unik untuk setiap video game yang mereka mainkan. Pengembang video game pada umumnya memberikan tingkat kesulitan yang bersifat statis sehingga tingkat kesulitan pemain pemula dengan pemain yang berpengalaman itu serupa. Hal ini menimbulkan video game menjadi tidak seimbang. Game balancing adalah salah satu aspek penting yang dapat meningkatkan minat seseorang untuk memainkan game tersebut dan secara adaptif dapat memberikan tingkat kesulitan yang dinamis bagi setiap pemain. Penelitian ini mengintegrasikan Dynamic Difficulty Adjustment (DDA) berbasis logika fuzzy untuk Procedural Content Generation (PCG) pada permainan roguelike sehingga dapat menghasilkan tingkat kesulitan yang dinamis. DDA akan mengolah parameter input dari keterampilan pemain ketika menyelesaikan map sebelumnya, seperti lama waktu, sisa darah, banyaknya serangan yang diterima, sisa peluru, akurasi pemain, dan jumlah musuh. Hasil dari DDA akan diolah kembali dengan menggunakan PCG untuk membuat map baru secara prosedural, seperti besar ruangan, jumlah musuh, jumlah item penyembuh, jumlah item peluru, dan jumlah tembok. Hal ini diharapkan dapat menciptakan penyesuaian kesulitan yang dinamis pada setiap map sesuai dengan keterampilan dari pemain. Dalam penelitian ini dilakukan juga perbandingan video game dengan tingkat kesulitan yang statis dan dengan tingkat kesulitan yang dinamis untuk dapat mengukur tingkat kepuasan dari pemain. Dari 30 responden, didapatkan hasil bahwa 80% pemain memiliki tingkat kepuasan yang lebih baik ketika memainkan video game dengan tingkat kesulitan yang dinamis

    Fibonacci Level Adjustment for Optimizing Player’s Performance and Engagement

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    Players’ engagement intensity in computer games is influenced by the level of difficulty the game offers. Traditional game-level plots adopt linear increases that sometimes do not match the users’ skill growth, causing boredom and hampering the users’ further skill growth. In this study, a nonlinear level adjustment scenario was proposed based on the Fibonacci sequence that provides gradual increases in the early stages of the games but more drastic changes in later phases. Here, the game’s difficulty level was automatically decided by a machine learning method. To test the proposed method, comparisons between four level adjustments in computer games: traditional plots, self-selected plots, linear adaptive plots, and the proposed nonlinear adaptive plots were run. The experiment was carried out with 40 testers. The experiment results show that the best player’s peak level in the proposed nonlinear adjustment was twice as high as that of linear adjustment. Also, the number of stages required to reach the peak under the proposed scenario was half that of linear games. This high playing performance goes hand in hand with deep playing engagement. The results demonstrate the efficiency of the proposed level adjustment algorithm

    IMPLEMENTASI DYNAMIC DIFFICULTY ADJUSTMENT PADA NON-VIOLENT VIDEO GAMES UNTUK MENINGKATKAN DAYA TARIK PEMAIN

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    Video Games merupakan sebuah permainan elektronik yang memiliki tujuan sebagai salah satu media hiburan pada zaman modern. Sebagai media hiburan, sebuah video games harus dapat membuat suasana pengalaman pada pemain agar termotivasi untuk menyelesaikan game tersebut. Dynamic Difficulty Adjustment merupakan sebuah metode yang digunakan untuk menyesuaikan tingkat kesulitan secara otomatis berdasarkan kemampuan yang dimiliki oleh pemain. Tujuan dari metode ini adalah agar pemain dapat mencapai flow state dimana pemain merasa termotivasi untuk melanjutkan permainan dan tidak menghadapi tingkat kesulitan yang terlalu tinggi sehingga menyebabkan pemain merasa frustrasi atau tingkat kesulitan yang terlalu rendah sehingga menyebabkan pemain merasa bosan. Tujuan dari penelitian ini adalah untuk meningkatkan daya tarik non-violent video games dengan menerapkan metode DDA. Pendekatan DDA yang akan digunakan adalah algoritma Hidden Markov Model (HMM) yang merupakan salah satu dari metode probabilitas. Game ini diuji dengan menggunakan kuesioner Game User Experience Satisfaction Scale (GUESS). Hasil pengujian menunjukan bahwa game yang diuji memiliki skor akhir yaitu 51,3 yang merupakan diatas rata-rata dan menunjukkan bahwa game tersebut cukup menarik. Video Games is an electronic game that has a purpose as one of the entertainment media in modern times. As an entertainment media, a video game must be able to create an atmosphere of experience where players will be motivated to complete the game. Dynamic Difficulty Adjustment is a method used to automatically adjust the difficulty level based on the abilities of the player. The purpose of this method is so that players can reach a flow state where players feel motivated to continue the game and do not face a level of difficulty that is too high, causing players to feel frustrated or a difficulty level that is too low, causing players to feel bored. The purpose of this study was to increase the attractiveness of non-violent video games by applying the DDA method. DDA approach that will be used is the Hidden Markov Model (HMM) algorithm which is one of the probabilities methods . This game was tested using the Game User Experience Satisfaction Scale (GUESS) questionnaire. The test results show that the game being tested has a final score of 51,3 which is above the average and indicates that the game is quite interesting

    Predicting Player Engagement in Tom Clancy's The Division 2: A Multimodal Approach via Pixels and Gamepad Actions

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    This paper introduces a large scale multimodal corpus collected for the purpose of analysing and predicting player engagement in commercial-standard games. The corpus is solicited from 25 players of the action role-playing game Tom Clancy's The Division 2, who annotated their level of engagement using a time-continuous annotation tool. The cleaned and processed corpus presented in this paper consists of nearly 20 hours of annotated gameplay videos accompanied by logged gamepad actions. We report preliminary results on predicting long-term player engagement based on in-game footage and game controller actions using Convolutional Neural Network architectures. Results obtained suggest we can predict the player engagement with up to 72% accuracy on average (88% at best) when we fuse information from the game footage and the player's controller input. Our findings validate the hypothesis that long-term (i.e. 1 hour of play) engagement can be predicted efficiently solely from pixels and gamepad actions.Comment: 8 pages, accepted for publication and presentation at 2023 25th ACM International Conference on Multimodal Interaction (ICMI

    Characterising Players of a Cube Puzzle Game with a Two-level Bag of Words

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    Ponencia presentada en UMAP '21: Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization, Utrecht (Netherlands), June 21 - 25, 2021This work explores an unsupervised approach for modelling players of a 2D cube puzzle game with the ultimate goal of customising the game for particular players based solely on their interaction data. To that end, user interactions when solving puzzles are coded as images. Then, a feature embedding is learned for each puzzle with a convolutional network trained to regress the players’ comple tion effort in terms of time and number of clicks. Next, the known bag-of-words technique is used at two levels. First, sets of puzzles are represented using the puzzle feature embeddings as the input space. Second, the resulting first-level histograms are used as input space for characterising players. As a result, new players can be characterised in terms of the resulting second-level histograms. Preliminary results indicate that the approach is effective for char acterising players in terms of performance. It is also tentatively observed that other personal perceptions and preferences, beyond performance, are somehow implicitly captured from behavioural data

    Dynamic difficulty adjustment of serious-game based on synthetic fog using activity theory model

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    This study used the activity theory model to determine the dynamic difficulty adjustment of serious-game based on synthetic fog. The difference in difficulty levels was generated in a 3-dimensional game environment with changes determined by applying varying fog thickness. The activity theory model in serious-games aims to facilitate development analysis in terms of learning content, the equipment used, and the resulting in-game action. The difficulty levels vary according to the player's ability because the game is expected to reduce boredom and frustration. Furthermore, this study simulated scenarios of various conditions, scores, time remaining, and the lives of synthetic players. The experimental results showed that the system can change the game environment with different fog thicknesses according to synthetic player parameters

    The Mood Game - How to use the player’s affective state in a shoot’em up avoiding frustration and boredom

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    In this demo paper, we present a shoot'em up game similar to Space Invaders called the "Mood Game" that incorporates players' affective state into the game mechanics in order to enhance the gaming experience and avoid undesired emotions like frustration and boredom. By tracking emotions through facial expressions combined with self-evaluation, keystrokes and performance measures, we have developed a game logic that adapts the playing difficulty based on the player's emotional state. The implemented algorithm automatically adjusts the enemy spawn rate and enemy behavior, the amount of obstacles, the number and type of power ups and the game speed to provide a smooth game play for different player skills. The effects of our dynamic game balancing mechanism will be tested in future work
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