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Towards a Distributed Multiagent Learning-Based Design Optimization Method

[+] Author Affiliations
Daniel Hulse, Brandon Gigous, Kagan Tumer, Christopher Hoyle, Irem Y. Tumer

Oregon State University, Corvallis, OR

Paper No. DETC2017-68042, pp. V02AT03A008; 14 pages
  • ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 2A: 43rd Design Automation Conference
  • Cleveland, Ohio, USA, August 6–9, 2017
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5812-7
  • Copyright © 2017 by ASME


Complex engineered systems create many design challenges for engineers and organizations because of the interactions between subsystems and desire for optimality. In some conceptual-level optimizations, the design problem is simplified to consider the most important variables in an all-in-one optimization framework. This work introduces a stochastic optimization method which uses a distributed multiagent design method in which action-value based learning agents make individual design choices for each component. These agents use a probabilistic action-selection strategy based on the learned objective values of each action. This distributed multiagent system is applied to a simple quadrotor optimization problem in an all-in-one optimization framework, and compared with the performance of centralized methods. Results show the multiagent system is capable of finding comparable designs to centralized methods in a similar amount of computational time. This demonstrates the potential merit of a multiagent approach for complex systems design.

Copyright © 2017 by ASME
Topics: Design , Optimization



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