We compare the performance of human players against that of the efficient global optimization (EGO) algorithm for 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 month from launch. We found that while only a small portion of human players can outperform the algorithm in the long term, players tend to formulate good heuristics early on that can be used to constrain the solution space. Such constraining of the search enhances algorithm efficiency, even for different game settings. These findings indicate that human-assisted computational searches are promising in solving comprehensible yet computationally hard optimal design and control problems, when human players can outperform the algorithm in a short term.
EcoRacer: Game-Based Optimal Electric Vehicle Design and Driver Control Using Human Players
Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received September 24, 2015; final manuscript received April 11, 2016; published online May 6, 2016. Assoc. Editor: Gary Wang.
- Views Icon Views
- Share Icon Share
- Cite Icon Cite
- Search Site
Ren, Y., Bayrak, A. E., and Papalambros, P. Y. (May 6, 2016). "EcoRacer: Game-Based Optimal Electric Vehicle Design and Driver Control Using Human Players." ASME. J. Mech. Des. June 2016; 138(6): 061407. https://doi.org/10.1115/1.4033426
Download citation file:
- Ris (Zotero)
- Reference Manager