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Towards a data-driven approach to scenario generation for serious games

机译：面向数据驱动的严肃游戏场景生成方法

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摘要

Serious games have recently shown great potential to be adopted in many applications, such as training and education. However, one critical challenge in developing serious games is the authoring of a large set of scenarios for different training objectives. In this paper, we propose a data-driven approach to automatically generate scenarios for serious games. Compared with other scenario generation methods, our approach leverages on the simulated player performance data to construct the scenario evaluation function for scenario generation. To collect the player performance data, an artificial intelligence (AI) player model is designed to imitate how a human player behaves when playing scenarios. The AI players are used to replace human players for data collection. The experiment results show that our data-driven approach provides good prediction accuracy on scenario's training intensities. It also outperforms our previous heuristic-based approach in its capability of generating scenarios that match closer to specified target player performance.Copyright (c) 2014 John Wiley & Sons, Ltd.
机译：严肃的游戏最近显示出在许多应用中被采用的巨大潜力，例如培训和教育。但是，开发严肃游戏的一个关键挑战是为不同的训练目标编写大量场景。在本文中，我们提出了一种数据驱动的方法来自动生成严肃游戏的场景。与其他场景生成方法相比，我们的方法利用模拟的玩家性能数据来构建场景评估功能。为了收集玩家的表现数据，设计了一个人工智能（AI）玩家模型来模仿人类玩家在玩场景时的行为。 AI播放器用于代替人工播放器进行数据收集。实验结果表明，我们的数据驱动方法对情景的训练强度提供了良好的预测准确性。在生成更接近指定目标玩家表现的场景的能力方面，它也优于我们以前的启发式方法。（c）2014 John Wiley＆Sons，Ltd.

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