Abstract
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. >
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Systems, Man, and Cybernetics
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
