Difference between revisions of "Cognitive Agent-Based Life Process Modelling to Predict Social Performance in Workplace Design"

From Design Computation
Jump to: navigation, search
(Keyphrases)
 
(2 intermediate revisions by the same user not shown)
Line 8: Line 8:
  
 
=Abstract=
 
=Abstract=
 +
Buildings are designed to enable high value human experience while being dynamically occupied for specific individual, interactive, and collaborative human functions, yet architects rarely collect data about how well their design performed from a social functionality perspective. As Frank Duffy said, “Because our heuristic seems to be ‘Never look back’, we are unable to predict the long-term consequences of what we design (Duffy 2008).” We present a cognitive agent-based simulation approach to predicting and evaluating the social performance of workplace design. Our simulation model is part of a larger multi-objective computational design framework for workplaces outlined below where we use it to evaluate social behaviour in workplaces in an iterative data driven process to achieve optimised multi-performance workplace design.The model is composed of three interrelated parts: Agent, Environment, and Data (I/O). Each Agent is an encapsulated object containing a dynamic internal state, visual perception, control mechanisms for path-finding / movement, and an autonomous decision making framework. A heterogeneous crowd of Agents take workplace related actions in relation to a workplace environment with other Agents, interactive destinations, zoning, planned, and unplanned events producing spatially situated social performance Data.
  
 
=Keywords=
 
=Keywords=
Line 16: Line 17:
  
 
=Topics=
 
=Topics=
 +
[[AI-driven development]], [[Artificial Intelligence in Design]], [[Automated Design Systems]], [[Immersive Workspaces]], [[Optimization]], [[Semiology]], [[Simulation]].
  
 
=Reference=
 
=Reference=
 
DOI: https://doi.org/10.47330/DCIO.2020.EIYA3557
 
DOI: https://doi.org/10.47330/DCIO.2020.EIYA3557
 +
 +
Video Presentation: https://youtu.be/EHmg2FWgwdQ
  
 
Full text: [https://www.designcomputation.org/dcio2020 Maciel, A. (Ed.), 2020. Design Computation Input/Output 2020, 1st ed. Design Computation, London, UK. ISBN: 978-1-83812-940-8, DOI:10.47330/DCIO.2020.QPRF9890]
 
Full text: [https://www.designcomputation.org/dcio2020 Maciel, A. (Ed.), 2020. Design Computation Input/Output 2020, 1st ed. Design Computation, London, UK. ISBN: 978-1-83812-940-8, DOI:10.47330/DCIO.2020.QPRF9890]

Latest revision as of 23:44, 14 December 2020


DC I/O 2020 poster by PATRIK SCHUMACHER, TYSON HOSMER, ZIMING HE, SOUNGMIN YU, SOBITHA RAVICHANDRAN


Abstract

Buildings are designed to enable high value human experience while being dynamically occupied for specific individual, interactive, and collaborative human functions, yet architects rarely collect data about how well their design performed from a social functionality perspective. As Frank Duffy said, “Because our heuristic seems to be ‘Never look back’, we are unable to predict the long-term consequences of what we design (Duffy 2008).” We present a cognitive agent-based simulation approach to predicting and evaluating the social performance of workplace design. Our simulation model is part of a larger multi-objective computational design framework for workplaces outlined below where we use it to evaluate social behaviour in workplaces in an iterative data driven process to achieve optimised multi-performance workplace design.The model is composed of three interrelated parts: Agent, Environment, and Data (I/O). Each Agent is an encapsulated object containing a dynamic internal state, visual perception, control mechanisms for path-finding / movement, and an autonomous decision making framework. A heterogeneous crowd of Agents take workplace related actions in relation to a workplace environment with other Agents, interactive destinations, zoning, planned, and unplanned events producing spatially situated social performance Data.

Keywords

Agent-Based Modelling, Artificial Intelligence, Data-Driven Design, Simulation, [[Behaviour], Computational Design, Generative Design, Parametric Design, Machine Learning, Life Process Modelling.

Keyphrases

thirst social wc motivation (100), internal state (60), public destination (60), home destination (50), interaction type (50), action utility factor (47), space occupied time (47), m max occupant (47), decision making (40), social performance (40), meeting table (40)

Topics

AI-driven development, Artificial Intelligence in Design, Automated Design Systems, Immersive Workspaces, Optimization, Semiology, Simulation.

Reference

DOI: https://doi.org/10.47330/DCIO.2020.EIYA3557

Video Presentation: https://youtu.be/EHmg2FWgwdQ

Full text: Maciel, A. (Ed.), 2020. Design Computation Input/Output 2020, 1st ed. Design Computation, London, UK. ISBN: 978-1-83812-940-8, DOI:10.47330/DCIO.2020.QPRF9890