By Topic

Robust action strategies to induce desired effects

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Haiying Tu ; Electr. & Comput. Eng. Dept., Univ. of Connecticut, Storrs, CT, USA ; Levchuk, Y.N. ; Pattipati, K.R.

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.

Published in:

Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on  (Volume:34 ,  Issue: 5 )