The post-CoViD building will have occupant-centric control with new communications features and efficient living and working environments. Come and discover the solutions deployed by industry majors and how they could benefit you. So – bring your lunch and lots of questions for the Q&A and join us to Connect, Interact and Learn. This is a virtual event via Zoom. The Zoom link and password will be provided via email two hours prior to the event to registered attendees.

The iconic Empire State Building in New York City celebrates its 90-year anniversary in a couple of years. By this age, people usually get wiser or grumpier, or both. The ESB is just getting smarter, proving New York is one of the smartest cities in the world. Initially, the renovation was expected to reduce energy consumption by 38 percent and reach $4.4 million return in annual energy savings. In reality, the project has been overrunning its own goals year by year.

Enter the 21st century building's "digital twin." Think of it as a dynamic, virtual model of the physical structure, powered by the massive amounts of data that a single structure generates around the clock--everything from design specs to equipment parameters and live occupancy data. With IoT-enabled sensors tracking a building's "pulse" and feeding data back into next-generation systems such as Watson, facility owners and managers today are able to reconstruct every relevant metric from a physical structure in a digital environment. Every asset--from the HVAC system to the vending machines--can be monitored and analyzed remotely. But how do you manage those assets over time?

Editor's Note: This piece was written by Casey Talon, a principal research analyst contributing to Navigant Research's Building Innovations program. The opinions represented in this piece are independent of Smart Cities Dive's views. Artificial intelligence (AI) is technology that learns. This concept encompasses machines that take in continuous data and adapt for perpetual evolution from the objective they were designed to achieve. Today, AI has captured mindshare and headlines, claiming market disruption across industries ranging from trucking to shopping.

Our hypothesis is that by equipping certain agents in a multi-agent system controlling an intelligent building with automated decision support, two important factors will be increased. The first is energy saving in the building. The second is customer value---how the people in the building experience the effects of the actions of the agents. We give evidence for the truth of this hypothesis through experimental findings related to tools for artificial decision making. A number of assumptions related to agent control, through monitoring and delegation of tasks to other kinds of agents, of rooms at a test site are relaxed. Each assumption controls at least one uncertainty that complicates considerably the procedures for selecting actions part of each such agent. We show that in realistic decision situations, room-controlling agents can make bounded rational decisions even under dynamic real-time constraints. This result can be, and has been, generalized to other domains with even harsher time constraints.