EcoRacer: Game-based optimal electric vehicle design and driver control using human players

Yi Ren, Alparslan Emrah Bayrak, Panos Y. Papalambros

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

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.

Original languageEnglish (US)
Title of host publication41st Design Automation Conference
PublisherAmerican Society of Mechanical Engineers (ASME)
Volume2A-2015
ISBN (Electronic)9780791857076
DOIs
StatePublished - 2015
EventASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2015 - Boston, United States
Duration: Aug 2 2015Aug 5 2015

Other

OtherASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2015
CountryUnited States
CityBoston
Period8/2/158/5/15

Fingerprint

Electric Vehicle
Electric vehicles
Driver
Game
Control Problem
Powertrains
Global optimization
Global Optimization
Optimization Algorithm
Optimal Control
NP-complete problem
Heuristics
Tend
Optimization Problem
Costs
Term
Design
Human

ASJC Scopus subject areas

  • Mechanical Engineering
  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Modeling and Simulation

Cite this

Ren, Y., Bayrak, A. E., & Papalambros, P. Y. (2015). EcoRacer: Game-based optimal electric vehicle design and driver control using human players. In 41st Design Automation Conference (Vol. 2A-2015). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/DETC201546836

EcoRacer : Game-based optimal electric vehicle design and driver control using human players. / Ren, Yi; Bayrak, Alparslan Emrah; Papalambros, Panos Y.

41st Design Automation Conference. Vol. 2A-2015 American Society of Mechanical Engineers (ASME), 2015.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ren, Y, Bayrak, AE & Papalambros, PY 2015, EcoRacer: Game-based optimal electric vehicle design and driver control using human players. in 41st Design Automation Conference. vol. 2A-2015, American Society of Mechanical Engineers (ASME), ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2015, Boston, United States, 8/2/15. https://doi.org/10.1115/DETC201546836
Ren Y, Bayrak AE, Papalambros PY. EcoRacer: Game-based optimal electric vehicle design and driver control using human players. In 41st Design Automation Conference. Vol. 2A-2015. American Society of Mechanical Engineers (ASME). 2015 https://doi.org/10.1115/DETC201546836
Ren, Yi ; Bayrak, Alparslan Emrah ; Papalambros, Panos Y. / EcoRacer : Game-based optimal electric vehicle design and driver control using human players. 41st Design Automation Conference. Vol. 2A-2015 American Society of Mechanical Engineers (ASME), 2015.
@inproceedings{d29e23a6b9f346caae2a88522c0ff348,
title = "EcoRacer: Game-based optimal electric vehicle design and driver control using human players",
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.",
author = "Yi Ren and Bayrak, {Alparslan Emrah} and Papalambros, {Panos Y.}",
year = "2015",
doi = "10.1115/DETC201546836",
language = "English (US)",
volume = "2A-2015",
booktitle = "41st Design Automation Conference",
publisher = "American Society of Mechanical Engineers (ASME)",

}

TY - GEN

T1 - EcoRacer

T2 - Game-based optimal electric vehicle design and driver control using human players

AU - Ren, Yi

AU - Bayrak, Alparslan Emrah

AU - Papalambros, Panos Y.

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

VL - 2A-2015

BT - 41st Design Automation Conference

PB - American Society of Mechanical Engineers (ASME)

ER -