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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...
Ubiquitous data from a variety of sources such as smart phones, vehicles equipped with GPS receivers and fixed sensors makes it an exciting time for the implementation of several Advanced Traffic Information and Management Systems (ATMS). Leveraging this data for current traffic state estimation along with short term predictions of traffic flow can have far reaching implications for the next generation...
In this paper, we present a dynamic adaptation framework to adapt the game scenarios for a multi-agent training game. We consider the problem where a trainee has to practice along multiple objectives during the training, and each objective is assigned with a desired difficulty level. The proposed dynamic adaptation approach takes into account of individual player's playing ability as well as the dynamic...
We report an approach to achieve effective crowd control strategies through adaptively evolving an agent-based model of Crowd Simulation for Military Operations (COSMOS). COSMOS is a complex system simulation platform developed to address challenges posed by the Military Operations in Urban Terrains (MOUT). Modeling and simulating soldiers' tactical behaviors in MOUT scenarios is challenging due to...
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