TY - GEN
T1 - BrainCrafter
T2 - IEEE Congress on Evolutionary Computation, CEC 2015
AU - Piskur, Jan
AU - Greve, Peter
AU - Togelius, Julian
AU - Risi, Sebastian
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/10
Y1 - 2015/9/10
N2 - This paper presents the online application Brain-Crafter, in which users can manually build artificial neural networks (ANNs) to control a robot in a maze environment. Users can either start to construct networks from scratch or elaborate on networks created by other users. In particular, BrainCrafter was designed to study how good we as humans are at building ANNs for control problems and if collaborating with other users can facilitate this process. The results in this paper show that (1) some users were in fact able to successfully construct ANNs that solve the navigation tasks, (2) collaboration between users presented difficulties and (3) the human-developed ANNs that managed to solve the task had certain regularities, suggesting that humans can use some of their intuition and spatial understanding in the design of ANNs. Most importantly, the initial results in this paper can serve as a starting point for investigating how to best combine human and machine design capabilities to create more complex artificial brains.
AB - This paper presents the online application Brain-Crafter, in which users can manually build artificial neural networks (ANNs) to control a robot in a maze environment. Users can either start to construct networks from scratch or elaborate on networks created by other users. In particular, BrainCrafter was designed to study how good we as humans are at building ANNs for control problems and if collaborating with other users can facilitate this process. The results in this paper show that (1) some users were in fact able to successfully construct ANNs that solve the navigation tasks, (2) collaboration between users presented difficulties and (3) the human-developed ANNs that managed to solve the task had certain regularities, suggesting that humans can use some of their intuition and spatial understanding in the design of ANNs. Most importantly, the initial results in this paper can serve as a starting point for investigating how to best combine human and machine design capabilities to create more complex artificial brains.
UR - http://www.scopus.com/inward/record.url?scp=84963532749&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84963532749&partnerID=8YFLogxK
U2 - 10.1109/CEC.2015.7257156
DO - 10.1109/CEC.2015.7257156
M3 - Conference contribution
AN - SCOPUS:84963532749
T3 - 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
SP - 2199
EP - 2206
BT - 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 25 May 2015 through 28 May 2015
ER -