摘要 :
Designing and developing games for learning is a difficult endeavor. Educational game designers must not only make an engaging and motivating game, but must also ensure that learning takes place as a result of gameplay. Educationa...
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Designing and developing games for learning is a difficult endeavor. Educational game designers must not only make an engaging and motivating game, but must also ensure that learning takes place as a result of gameplay. Educational researchers have sought to define design principles in order to lessen the difficulty involved with game design. In spite of these efforts, there is still a paucity of empirical research in support of significant direct learning gains that result from time spent in a game environment. This study investigated the effectiveness of a design and development approach centered on playtesting, with the purpose of ensuring the proper intrinsic integration of multiplication properties, concepts, and strategies within the game’s mechanics.
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摘要 :
This paper describes a method for generative player modeling and its application to the automatic testing of game content using archetypal player models called procedural personas. Theoretically grounded in psychological decision ...
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This paper describes a method for generative player modeling and its application to the automatic testing of game content using archetypal player models called procedural personas. Theoretically grounded in psychological decision theory, procedural personas are implemented using a variation of Monte Carlo tree search (MCTS) where the node selection criteria are developed using evolutionary computation, replacing the standard UCB1 criterion of MCTS. Using these personas, we demonstrate how generative player models can be applied to a varied corpus of game levels and demonstrate how different playstyles can be enacted in each level. In short, we use artificially intelligent personas to construct synthetic playtesters. The proposed approach could be used as a tool for automatic play testing when human feedback is not readily available or when quick visualization of potential interactions is necessary. Possible applications include interactive tools during game development or procedural content generation systems where many evaluations must he conducted within a short time span.
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Think-alouds are a common method of collecting design data where a player describes their play for a facilitator. Games promote a feeling of immersivity and player presence, which is in tension with traditional think-aloud methods...
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Think-alouds are a common method of collecting design data where a player describes their play for a facilitator. Games promote a feeling of immersivity and player presence, which is in tension with traditional think-aloud methods. This work introduces a new type of think-aloud protocol intended for game-based contexts which leverages the genres of video blogging and livestreaming in game culture. This new approach, called Play Aloud testing, has participants take on the role of a game streamer by expressing their thoughts, feelings, and experiences as they play – modeled after live streaming commentary. This paper demonstrates the potential of the Play Aloud approach using playtest data from a game called HEX of the Turtle Islands. We highlight how Play Aloud testing generated useful data providing insight into the experience of young players in a way that was authentic to the format of digital games and consistent with youth gaming practices.
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The emerging field of game user research (GUR) investigates interaction between players and games and the surrounding context of play. Game user researchers have explored methods from, for example, human-computer interaction, psyc...
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The emerging field of game user research (GUR) investigates interaction between players and games and the surrounding context of play. Game user researchers have explored methods from, for example, human-computer interaction, psychology, interaction design, media studies, and the social sciences. They've extended and modified these methods for different types of digital games, such as social games, casual games, and serious games. This article describes several current GUR methods. A case study illustrates two specific methods: think-aloud and heuristics.
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Recently, there have been several high-profile achievements of agents learning to play games against humans and beat them. In this article, we study the problem of training intelligent agents in service of game development. Unlike...
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Recently, there have been several high-profile achievements of agents learning to play games against humans and beat them. In this article, we study the problem of training intelligent agents in service of game development. Unlike the agents built to "beat the game," our agents aim to produce human-like behavior to help with game evaluation and balancing. We discuss two fundamental metrics based on which we measure the human-likeness of agents, namely skill and style, which are multifaceted concepts with practical implications outlined in this article. We report four case studies in which the style and skill requirements inform the choice of algorithms and metrics used to train agents; ranging from A* search to state-of-the-art deep reinforcement learning (RL). Furthermore, we, show that the learning potential of state-of-the-art deep RL models does not seamlessly transfer from the benchmark environments to target ones without heavily tuning their hyperparameters, leading to linear scaling of the engineering efforts, and computational cost with the number of target domains.
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