Conference Paper

Towards Automatic Personalized Content Generation for Platform Games

Conference: Proceedings of the Sixth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2010, October 11-13, 2010, Stanford, California, USA
Source: DBLP


In this paper, we show that personalized levels can be automatically generated for platform games. We build on previous work, where models were derived that predicted player experience based on features of level design and on playing styles. These models are constructed using preference learning, based on questionnaires administered to players after playing different levels. The contributions of the current paper are (1) more accurate models based on a much larger data set; (2) a mechanism for adapting level design parameters to given players and playing style; (3) evaluation of this adaptation mechanism using both algorithmic and human players. The results indicate that the adaptation mechanism effectively optimizes level design parameters for particular players. 1

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Available from: Julian Togelius, Oct 07, 2015
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    • "Ingeniería y Ciencia Alejandro Cannizzo and Esmitt Ramírez game genre [9],[10]. For instance, distinguished examples such as the optimization of tracks in car racing games [11], weapons generation for space shooter games [12], rulesets for board and predator-prey games [13] [14], automatic levels based on playing style for platform games [15],[16], music generation according to the genre or mood of games [17], and others. "
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    ABSTRACT: we present an approach to create assets using procedural algorithms in maps generation and dynamic adaptation of characters for a MOBA video game, preserving the balancing feature to players. Maps are created based on offering equal chances of winning or losing for both teams. Also, a character adaptation system is developed which allows changing the attributes of players in real-time according to their behaviour, always maintaining the game balanced. Our tests show the effectiveness of the proposed algorithms to establish the adequate values in a MOBA video game.
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    • "Several features have been directly extracted from the data recorded. Most of these features appear in our previous studies [40], [41], [42] and their selection is made in order to be able to represent the difference between a large variety of Super Mario Bros playing styles. The full list of gameplay features is presented in Table I "
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    ABSTRACT: Estimating affective and cognitive states in conditions of rich human-computer interaction, such as in games, is a field of growing academic and commercial interest. Entertainment and serious games can benefit from recent advances in the field as, having access to predictors of the current state of the player (or learner) can provide useful information for feeding adaptation mechanisms that aim to maximize engagement or learning effects. In this paper, we introduce a large data corpus derived from 58 participants that play the popular Super Mario Bros platform game and attempt to create accurate models of player experience for this game genre. Within the view of the current research, features extracted both from player gameplay behavior and game levels, and player visual characteristics have been used as potential indicators of reported affect expressed as pairwise preferences between different game sessions. Using neuroevolutionary preference learning and automatic feature selection, highly accurate models of reported engagement, frustration, and challenge are constructed (model accuracies reach 91%, 92%, and 88% for engagement, frustration, and challenge, respectively). As a step further, the derived player experience models can be used to personalize the game level to desired levels of engagement, frustration, and challenge as game content is mapped to player experience through the behavioral and expressivity patterns of each player.
    12/2013; 43(6):1519-1531. DOI:10.1109/TCYB.2013.2271738
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    • "Beyond variation within a game, some authors have looked at automatically creating novel game content tailored to players . For instance, building on the player model developed in [16], Shaker et al. automatically generate platform game levels [18]. Hastings et al. log weapon use, and employ the data to dynamically evolve novel particle system-based weapons adapted to the player [19]. "
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    ABSTRACT: Computational analysis of player style has significant potential for video game design: it can provide insights into player behavior, as well as the means to dynamically adapt a game to each individual's style of play. To realize this potential, computational methods need to go beyond considerations of challenge and ability and account for aesthetic aspects of player style. We describe here a semiautomatic unsupervised learning approach to modeling player style using multiclass linear discriminant analysis (LDA). We argue that this approach is widely applicable for modeling player style in a wide range of games, including commercial applications, and illustrate it with two case studies: the first for a novel arcade game called Snakeotron, and the second for Rogue Trooper, a modern commercial third-person shooter video game.
    IEEE Transactions on Computational Intelligence and AI in Games 09/2012; 4(3):152-166. DOI:10.1109/TCIAIG.2012.2213600 · 1.48 Impact Factor
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