By Topic

Learning automata approach to hierarchical multiobjective analysis

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

2 Author(s)
Narendra, K.S. ; Dept. of Electr. Eng., Yale Univ., New Haven, CT, USA ; Parthasarathy, K.

A novel approach to hierarchical multiobjective analysis using the theory of learning automata is introduced. The problem is modeled as several hierarchies of automata involved in stochastic identical payoff games at the various levels. It is shown that if suitable learning algorithms are chosen at all the levels, the overall performance of the system will improve at each stage. The relevance of the model to multilevel optimization problems is illustrated by considering a simple problem of labeling images consistently

Published in:

Systems, Man and Cybernetics, IEEE Transactions on  (Volume:21 ,  Issue: 1 )