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Overview: Artificial Intelligence in Performance-Driven Design: Theories, Methods, and Tools explores the application of Artificial Intelligence (AI), specifically Machine Mearning (ML), for performance modeling within the built environment. This work develops the theoretical foundations and methodological frameworks for utilizing AI/ML, with an emphasis on multi-scale modeling encompassing energy flows, environmental quality, and human systems. Artificial intelligence (AI) has emerged as a ubiquitous force in today’s world, asserting its influence across an expansive spectrum of human endeavors. AI “seeks to make computers do the sorts of things that minds can do”. At its core, AI endeavors to engineer computational entities capable of performing tasks traditionally associated with human cognition and to facilitate a synthetic replication of human abilities such as communication, learning, perception, problem‐solving, and reasoning. In pursuit of this, the discipline is methodically segmented into distinct yet interconnected subfields, for example: Machine Learning (ML), which learns from data to enable informed decision‐making; natural language processing (NLP), which deciphers and constructs human language; computer vision (CV), which interprets visual data; and robotics, which imparts autonomy to machines for task execution. These diverse subfields underpin AI’s quest to forge machines that operate with a semblance of human intelligence, enhancing human capacities and spearheading a revolution in technological innovation. Across the various chapters in this book, ML and its application to the built environment offers an array of methodologies tailored to distinct learning paradigms, each leveraging data to enhance design, planning, and operational efficiencies.
Genre: Non-Fiction > Tech & Devices
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