Collective Construction Modeling and Machine Learning: Potential for Architectural Design

被引:0
作者
Narahara, Taro [1 ]
机构
[1] New Jersey Inst Technol, Newark, NJ 07102 USA
来源
ECAADE 2017: SHARING OF COMPUTABLE KNOWLEDGE! (SHOCK!), VOL 1 | 2017年
关键词
Design tools; Stigmergy; Machine learning;
D O I
暂无
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Recently, there are significant developments in artificial intelligence using advanced machine learning algorithms such as deep neural networks. These new methods can defeat human expert players in strategy-based board games such as Go and video games such as Breakout. This paper suggests a way to incorporate such advanced computing methods into architectural design through introducing a simple conceptual design project inspired by computational interpretations of wasps' collective constructions. At this stage, the paper's intent is not to introduce a practical and fully finished tool directly useful for architectural design. Instead, the paper proposes an example of a program that can potentially become a conceptual framework for incorporating such advanced methods into architectural design.
引用
收藏
页码:593 / 600
页数:8
相关论文
共 8 条
  • [1] [Anonymous], 2015, CORR
  • [2] [Anonymous], NATURE
  • [3] Three-dimensional architectures grown by simple 'stigmergic' agents
    Bonabeau, E
    Guérin, S
    Snyers, D
    Kuntz, P
    Theraulaz, G
    [J]. BIOSYSTEMS, 2000, 56 (01) : 13 - 32
  • [4] Grasse P. P., 1959, Insectes Sociaux Paris, V6, P41, DOI 10.1007/BF02223791
  • [5] Narahara T., 2008, P ASS COMP AID DES A, P324
  • [6] Shimizu R., 2016, DEEP LEARNING PROGRA
  • [7] Mastering the game of Go with deep neural networks and tree search
    Silver, David
    Huang, Aja
    Maddison, Chris J.
    Guez, Arthur
    Sifre, Laurent
    van den Driessche, George
    Schrittwieser, Julian
    Antonoglou, Ioannis
    Panneershelvam, Veda
    Lanctot, Marc
    Dieleman, Sander
    Grewe, Dominik
    Nham, John
    Kalchbrenner, Nal
    Sutskever, Ilya
    Lillicrap, Timothy
    Leach, Madeleine
    Kavukcuoglu, Koray
    Graepel, Thore
    Hassabis, Demis
    [J]. NATURE, 2016, 529 (7587) : 484 - +
  • [8] Sutton R., 1998, Introduction to reinforcement learning