Robust Action Strategies to Induce Desired Effects

摘要

This paper provides a new methodology for obtaining a near-optimal strategy (i.e., specification of courses of action over time) for achieving the desired effects in a mission environment that also is robust to environmental perturbations (i.e., unexpected events and/or parameter uncertainties). 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, the authors 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.