Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Evolutionary Computation
Automated red teaming: a proposed framework for military application
Proceedings of the 9th annual conference on Genetic and evolutionary computation
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Research advances in automated red teaming
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
Transactions on computational science XII
A Computational Model of Emotions for Agent-Based Crowds in Serious Games
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Agent-Based simulation platform evaluation in the context of human behavior modeling
AAMAS'11 Proceedings of the 10th international conference on Advanced Agent Technology
Evolvable simulations applied to automated red teaming: a preliminary study
Proceedings of the Winter Simulation Conference
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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 the complex and emerging behaviors of crowds and large parameter space of the models. Consequently, it is difficult to search for effective crowd control strategies through tuning the model parameters manually. We employ an adaptive evolutionary computation approach, using the Complex Adaptive Systems Evolver (CASE), to address this challenge. Specifically, we conduct experiments using a "building-protection" scenario, where the operation plans of soldier agents are adaptively evolved to best control a crowd. The results suggest this approach using agent-based simulation and evolutionary computation techniques is promising for the study of complex military operations.