Robust Action Strategies to Induce Desired Effects
CONNECTICUT UNIV STORRS DEPT OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE
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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 andor 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.
- Administration and Management
- Statistics and Probability
- Operations Research