Title :
Robust action strategies to induce desired effects
Author :
Tu, Haiying ; Levchuk, Yuri N. ; Pattipati, Krishna R.
Author_Institution :
Electr. & Comput. Eng. Dept., Univ. of Connecticut, Storrs, CT, USA
Abstract :
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. A genetic algorithm is applied to search for a near-optimal strategy with DBN serving as a fitness evaluator. The joint probability of achieving the desired effects (namely, the probability of success) at specified times is a random variable due to uncertainties in the environment. Consequently, we focus on signal-to-noise ratio (SNR), a measure of the mean and variance of the probability of success, to gauge the goodness of a strategy. The resulting strategy will not only have a high likelihood of inducing the desired effects, but will also be robust to environmental uncertainties.
Keywords :
belief networks; genetic algorithms; stochastic processes; uncertainty handling; dynamic Bayesian network; effects-based operations; genetic algorithm; robust action strategy; signal-to-noise ratio; stochastic mission model; Artificial intelligence; Bayesian methods; Genetic algorithms; Motion planning; Random variables; Robustness; Signal to noise ratio; Stochastic processes; Uncertain systems; Uncertainty; DBNs; Dynamic Bayesian networks; EBOs; GAs; SNR; Taguchi method; effects-based operations; genetic algorithms; optimization; organizational design; robustness; signal-to-noise ratio;
Journal_Title :
Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on
DOI :
10.1109/TSMCA.2004.832823
