Clustering Agent Optimization Results in Dynamic Scenarios

The application of optimization algorithms to parameter driven simulations and agents has been thoroughly explored in literature. However, classical optimization algorithms do not take into account the fact that simulations normally have dynamic scenarios. This paper analyzes the possibility of using the classical optimization methods, combined with clustering techniques, in order to optimize parameter driven agents, in simulations having dynamic scenarios. This will be accomplished by optimizing the agents in several random static scenarios and clustering the optimum results of each of these optimizations in order to find a set of typical solutions for the agent parametrization problem. These typical solutions can then be used in dynamic scenario simulations as references that will help the agents adapt to scenario changes. The results of this approach show that, in some cases, it is possible to improve the outcome of simulations in dynamic environments while still using the classical methods developed for static scenarios.