A multi agent systems for design simulation framework: experiments with virtual physical social feedback for architecture

This paper presents research on the development of multi-agent systems (MAS) for integrated and performance driven architectural design. It presents the development of a simulation framework that bridges architecture and engineering, through a series of multi-agent based experiments. The research is motivated to combine multiple design agencies into a system for managing and optimizing architectural form, across multiple objectives and contexts. The research anticipates the incorporation of feedback from real world human behavior and user preferences with physics based structural form finding and environmental analysis data. The framework is a multi-agent system that provides design teams with informed design solutions, which simultaneously optimize and satisfy competing design objectives. The initial results for building structures are measured in terms of the level of lighting improvements and qualitatively in geometric terms. Critical to the research is the elaboration of the system and the feedback loops that are possible when using the multi-agent systems approach.

[1]  David Jason Gerber,et al.  Designing-in performance: A framework for evolutionary energy performance feedback in early stage design , 2014 .

[2]  Leandro Soriano Marcolino,et al.  Agents Vote for the Environment: Designing Energy-Efficient Architecture , 2015, AAAI Workshop: Computational Sustainability.

[3]  Davide Schaumann,et al.  Modelling and Simulating Use Processes in Buildings , 2013 .

[4]  Axel Kilian,et al.  PARTICLE-SPRING SYSTEMS FOR STRUCTURAL FORM FINDING , 2005 .

[5]  Jakob Beetz,et al.  Towards a Multi Agent System for the Support of Collaborative Design - Assembling a toolbox for the creation of a proof of concept , 2004 .

[6]  Sean Hanna,et al.  Book Review: Paradigms in Computing: Making, Machines, and Models for Design Agency in Architecture , 2015 .

[7]  Amarjit Singh Creative Systems in Structural and Construction Engineering , 2017 .

[8]  Nicholas R. Jennings,et al.  A Roadmap of Agent Research and Development , 2004, Autonomous Agents and Multi-Agent Systems.

[9]  Luís Ferreira Pires,et al.  Architectural Design , 2016, Springer International Publishing.

[10]  Kostas Terzidis,et al.  Algorithmic Architecture , 2006 .

[11]  A. Malkawi,et al.  Optimizing building form for energy performance based on hierarchical geometry relation , 2009 .

[12]  L. An,et al.  Modeling human decisions in coupled human and natural systems : Review of agent-based models , 2012 .

[13]  Jeffrey Huang,et al.  Parametric practices: models for design exploration in architecture , 2007 .

[14]  Ahmet Çakir,et al.  Human Factors in Lighting , 2014, Behav. Inf. Technol..

[15]  David Jason Gerber,et al.  Design optioneering: multi-disciplinary design optimization through parameterization, domain integration and automation of a genetic algorithm , 2012, ANSS 2012.

[16]  Li An,et al.  Modeling human decisions in coupled human and natural systems: Review of agent-based models , 2012 .

[17]  Arsalan Heydarian,et al.  Immersive Virtual Environments: Experiments on Impacting Design and Human Building Interaction , 2014, CAADRIA proceedings.

[18]  Yehuda E. Kalay,et al.  Performance-based design , 1999 .

[19]  Achim Menges,et al.  Morphospaces of Robotic Fabrication , 2013 .