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

Luo, Linbo and Yin, Haiyan and Cai, Wentong and Lees, Michael and Othman, Nasri Bin and Zhou, Suiping (2014) Towards a data-driven approach to scenario generation for serious games. Computer Animation and Virtual Worlds, 25 (3-4). pp. 393-402. ISSN 1546-4261

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Abstract

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.

Item Type: Article
Additional Information: Article first published online: 19 May 2014
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 15845
Notes on copyright: This is the peer reviewed version of the following article: Luo, L., Yin, H., Cai, W., Lees, M., Othman, N. B. and Zhou, a. S. (2014), Towards a data-driven approach to scenario generation for serious games. Comp. Anim. Virtual Worlds, 25: 393–402. doi: 10.1002/cav.1588, which has been published in final form at http://dx.doi.org/10.1002/cav.1588. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
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Depositing User: Suiping Zhou
Date Deposited: 18 Sep 2015 09:40
Last Modified: 06 Dec 2016 10:05
URI: http://eprints.mdx.ac.uk/id/eprint/15845

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