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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...
This paper proposes a newevolutionary algorithm-based methodology for optimal crowdevacuation planning. In the proposed methodology, a heuristic-based evacuation scheme is firstly introduced. The key idea is to divide the region into a set of sub-regions and use a heuristic rule to dynamically recommend an exit to agents in each sub-region. Then, an evolutionary framework based on the Cartesian Genetic...
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...
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