TY - GEN
T1 - EcoRacer
T2 - ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2015
AU - Ren, Yi
AU - Bayrak, Alparslan Emrah
AU - Papalambros, Panos Y.
N1 - Funding Information:
This work has been supported by the National Science Foundation under Grant No. CMMI-1266184. This support is gratefully acknowledged. We also thank members of the Optimal Design Laboratory at the University of Michigan for suggestions and advice on game design and experimentation, and all our players for making this paper possible.
Publisher Copyright:
Copyright © 2015 by ASME.
Copyright:
Copyright 2016 Elsevier B.V., All rights reserved.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84978957722&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84978957722&partnerID=8YFLogxK
U2 - 10.1115/DETC201546836
DO - 10.1115/DETC201546836
M3 - Conference contribution
AN - SCOPUS:84978957722
T3 - Proceedings of the ASME Design Engineering Technical Conference
BT - 41st Design Automation Conference
PB - American Society of Mechanical Engineers (ASME)
Y2 - 2 August 2015 through 5 August 2015
ER -