A huge wildfire rages in the wilderness of Arizona’s White Mountains. The blaze scorches asphalt and damages area bridges, causing traffic accidents when residents attempt to evacuate. As hospitals are flooded with patients, power lines succumb to the flames, leaving doctors working in the dark. Later, farmers in the state discover smoke damage to crops, sending food prices soaring.
This hypothetical scenario is an example of a cascading disaster — when an unexpected event occurs and triggers a series of calamities.
Now, the Arizona Board of Regents, or ABOR, has taken an important step in helping the state improve its readiness to face such scenarios. This month, the governing body for Arizona’s public universities awarded a $1.7 million research grant to the Department of Emergency and Military Affairs, or DEMA. As part of the grant, a team of experts at Arizona State University will develop software powered by artificial intelligence, or AI, to assist emergency responders in predicting and managing disaster scenarios.
The project is led by Maj. Gen. Kerry Muehlenbeck, director of DEMA and adjutant general of Arizona. Paulo Shakarian, an associate professor of computer science and engineering in the School of Computing and Augmented Intelligence, part of the Ira A. Fulton Schools of Engineering at ASU, is overseeing the work being done at the university.
Shakarian is a leading voice in the field of neurosymbolic AI, the scientific push to create forms of artificial intelligence that are trustworthy and produce reliable results. As a former commissioned officer in the U.S. Army and recipient of the Bronze Star, he brings a distinct perspective to the work.
Shakarian explains that the project will apply several ideas from the field of AI, including logic programming and machine learning, where computers process huge sets of data about the past to calculate the probability of future events while authenticating the predictions. The results of this analysis will be used to create algorithms, or instructions the software uses to do its work. The end goal is to help emergency planners have the right resources in the right place at the right time.
“Second and third-order effects, or the mid and long-term consequences of disasters, are very difficult to account for when you do emergency management planning,” Shakarian says. “One of the important ideas here is that the algorithms need to anticipate these effects so that state planners can pre-position assets to get ahead of problems that can arise.”
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