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ecoRacer: Game-Based Optimal Electric Vehicle Design and Driver Control Using Human Players

[+] Author Affiliations
Yi Ren

Arizona State University, Tempe, AZ

Alparslan Emrah Bayrak, Panos Y. Papalambros

University of Michigan, Ann Arbor, MI

Paper No. DETC2015-46836, pp. V02AT03A009; 10 pages
doi:10.1115/DETC2015-46836
From:
  • ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 2A: 41st Design Automation Conference
  • Boston, Massachusetts, USA, August 2–5, 2015
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5707-6
  • Copyright © 2015 by ASME

abstract

We investigate the cost and benefit of crowdsourcing solutions to an NP-complete powertrain design and control problem. Specifically, we cast this optimization problem as an online competition, and received 2391 game plays by 124 anonymous players during the first week from the launch. We compare the performance of human players against that of the Efficient Global Optimization (EGO) algorithm. We show that while only a small portion of human players can outperform the algorithm in long term, players tend to formulate good heuristics early on, from where good solutions can be extracted and used to constrain the solution space. Incorporating this constraint into the search enhances the efficiency of the algorithm, even for problem settings different from the game. These findings indicate that human computation is promising in solving comprehensible and computationally hard optimal design and control problems.

Copyright © 2015 by ASME

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