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首页> 外文期刊>Computational Intelligence and AI in Games, IEEE Transactions on >Design and Evaluation of a Data-Driven Scenario Generation Framework for Game-Based Training
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Design and Evaluation of a Data-Driven Scenario Generation Framework for Game-Based Training

机译：基于游戏的训练的数据驱动方案生成框架的设计和评估

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摘要

Generating suitable game scenarios that can cater for individual players has become an emerging challenge in procedural content generation. In this paper, we propose a data-driven scenario generation framework for game-based training. An evolutionary scenario generation process is designed with a fitness evaluation methodology that integrates the processes of AI player modeling, simulation and model training based on artificial neural networks. The fitness function for scenario evaluation can be automatically constructed based on the proposed methodology. To further enhance the evaluation of scenarios, we specifically study the impact of the timing of events in a scenario and propose a generic scenario representation model that characterizes individual scenario based on the types and timing of events in the scenario. We present an extensive evaluation of our framework by validating our AI player model, demonstrating the impact of timing of events in a scenario and comparing the effectiveness of our data-driven framework with our previous heuristic-based approach and a random baseline. The results show that it is necessary to consider the timing of events for scenario evaluation and the proposed framework works well in generating scenarios for game-based training.
机译：产生适合个人玩家的合适游戏场景已成为程序内容生成中的新兴挑战。在本文中，我们提出了一种基于游戏的训练的数据驱动场景生成框架。使用适应性评估方法设计演化场景生成过程，该方法将基于人工神经网络的AI玩家建模，仿真和模型训练过程集成在一起。情景评估的适应度函数可以根据所提出的方法自动构建。为了进一步增强对场景的评估，我们专门研究了场景中事件时间的影响，并提出了一个通用的场景表示模型，该模型基于场景中事件的类型和时间来表征单个场景。我们通过验证我们的AI播放器模型，演示场景中事件时序的影响以及将数据驱动框架的有效性与我们以前的基于启发式方法和随机基准进行比较，对框架进行了广泛的评估。结果表明，需要考虑事件的时机以进行场景评估，并且所提出的框架在为基于游戏的训练生成场景时效果很好。

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