Paper Info
Title
Design And Evaluation Of A Data-Driven Scenario Generation Framework For Game-Based Training
Abstract
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.
Year
DOI
Venue
2017
10.1109/TCIAIG.2016.2541168
IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES
Keywords
Field
DocType
Content creation, game-based training, genetic algorithms, neural networks, procedural content
Content generation,Data modeling,Heuristic,Data-driven,Computer science,Simulation,Fitness function,Artificial intelligence,Artificial neural network,Machine learning
Journal
Volume
Issue
ISSN
9
3
1943-068X
Citations 
PageRank 
References 
2
0.36
19
Authors
5
Name
Order
Citations
PageRank
Linbo Luo1537.54
Haiyan Yin2545.71
Wentong Cai31928197.81
Jing-hui Zhong438033.00
Michael Lees5586.48