Machine Learning and the City
Abstract
Machine Learning and the City: Applications in Architecture and Urban Design delivers a robust exploration of machine learning (ML) and artificial intelligence (AI) in the context of the built environment. Relevant contributions from leading scholars in their respective fields describe the ideas and techniques that underpin ML and AI, how to begin using ML and AI in urban design, and the likely impact of ML and AI on the future of city design and planning.
Each section couples theoretical and technical chapters, authoritative references, and concrete examples and projects that illustrate the efficacy and power of machine learning in urban design. The book also includes:
- An introduction to the probabilistic logic that underpins machine learning
- Comprehensive explorations of the applications of machine learning and artificial intelligence to urban environments
- Practical discussions of the consequences of applied machine learning and the future of urban design
My humble contribution to the book is Chapter 12: Autonomous Artificial Intelligent Agents. In this chapter, I’m illustrating how genetic algorithms, and NEAT (neuroevolution of augmented topologies) in particular, can be used to evolve intelligent agents that can become the foundational building blocks of modern smart city infrastructure.
The book is now awailable at:
- Willey: https://www.wiley.com/en-gb/Machine+Learning+and+the+City%3A+Applications+in+Architecture+and+Urban+Design-p-9781119749639
- Amazon: https://www.amazon.com/gp/aw/d/B0B457HK36/ref=tmm_kin_swatch_0?ie=UTF8&qid=&sr=
- Google: https://books.google.com.ua/books?id=phh1EAAAQBAJ&lpg=PP1&pg=PP1#v=onepage&q&f=false
Iaroslav Omelianenko
CTO/Research Director at NewGround LLC
My research interests include genetic algorithms/neuroevolution, synthetic cognitive systems, smart environment, and cooperative robotics.