Robust Action Strategies to Induce Desired Effects Student paper submission Submit to: 2002 CCRTS --- Modeling and Simulation Authors:

A new methodology is given in this paper to obtain a near-optimal strategy (i.e., specification of courses of action over time), which is also robust to environmental perturbations (unexpected events and/or parameter uncertainties), to achieve the desired effects. A dynamic Bayesian network (DBN)-based stochastic mission model is employed to represent the dynamic and uncertain nature of the environment. Genetic algorithms are applied to search for a near-optimal strategy with DBN serving as a fitness evaluator. The probability of achieving the desired effects (namely, the probability of success) at a specified terminal time is a random variable due to uncertainties in the environment. Consequently, we focus on signal-to-noise ratio (SNR), a measure of mean and variance of the probability of success, to gauge the goodness of a strategy. The resulting strategy will not only have a relatively high probability of inducing the desired effects, but also be robust to environmental uncertainties.