Quantitative ways of measuring client’s preferences: A step toward creating an intelligent architectural design agent

Technology-driven and digital modeling developments are undeniably changing design and construction professions across the architecture, engineering and construction (AEC) industry. Today, it is impossible to conceive of an architectural practice without using computer tools right from initial design conceptualization to the creation of construction drawings for a given project. The computer applications delivered new capabilities to automate the design process and offered various possibilities of assistant to the designer. However, they are not smart enough yet to understand clients or end users' (e.g. building occupants) needs or expectations and innovate a building design. Artificial Intelligence (AI) could be a solution to making architectural and building design process less time-demanding and more organized with minimal manual interference. The ultimate aim of this study is to make AI part of AEC’s digital journey, by making an intelligent agent able to both understand client needs and produce building designs. An intelligent design agent is defined as an autonomous entity that perceives client needs and makes design decisions towards achieving client satisfaction while balancing design and technical objectives. There are two challenges with respect to creating intelligent design agents; (1) finding quantitative ways of measuring client’s needs and preferences, and (2) providing a mathematical framework for making design decisions. This study focuses primarily on the first challenge and proposes a solution for creating measurable data about the client’s needs and preferences. First, an overview of technologies that support quantitative measurement of people's physical, emotional, and behavioral characteristics is presented. These technologies include eye tracking, facial expressions, electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), and galvanic skin response (GSR). This overview will enable us to understand potential applications of psychological measurement in the AI domain. This will be followed by a hypothetical design experiment to test the hypothesis that we can determine a subject’s degree of satisfaction by recording and analyzing his or her eye-movements and facial expressions when presented with a collection of visual data, like window design options on a screen. The participants are provided with window design options and asked to evaluate each design based on their preference by giving it a score. While study subjects complete evaluation tasks for each design option, we record their interactions using eye-tracking and an automated facial expression tool. The objective is to find a relationship between subjects’ preferences (as the independent variables) and the emotion expressed and the time spent on each design (as the dependent variables). This will allow us to provide a first look at how subjects interact with different attributes. © 2018 The Authors. Published by Diamond Congress Ltd. Peer-review under responsibility of the scientific committee of the Creative Construction Conference 2018.