Teaching AI about social intelligence for better teamwork

Artificial intelligence can make quick, lifesaving decisions — such as finding the fastest route to victims in a collapsed building — but it cannot understand why the rescue team diverts from that route or predict how the team might act in certain situations.

Current AI is a good tool but a poor teammate because of its lack of human understanding.

That is why the research and development arm of the U.S. Department of Defense, the Defense Advanced Research Projects Agency (DARPA), is exploring new ways to build social skills in AI systems that would allow humans and machines to work effectively together.

Clues on how to create machines with social intelligence have been found in an unexpected place: the popular video game Minecraft.

Arizona State University researchers teamed up with Aptima during a four-year program funded by DARPA called Artificial Social Intelligence for Successful Teams, or ASIST. The project aims to improve the social intelligence of artificial intelligence and make it better able to assist teams of humans working in complex environments, including national security missions.

Researchers from the Center for Human, Artificial Intelligence, and Robot Teaming (CHART) —part of ASU’s Global Security Initiative — generated data from 1,160 Minecraft games, which represents the largest publicly available human-AI team research dataset in history.

CHART synthesizes research across computer science, robotics, law, art and social science to create humanmachine systems that will revolutionize how we ensure national security.

Minecraft allowed researchers to design complex and highly dynamic tasks in a simulated urban search and rescue

mission. In one scenario, a fire sweeps through a small town, putting people’s homes and lives at risk. The response team arrives and must operate as a unit to make quick, lifesaving decisions.

But what happens if one member of the team is not a person, but a virtual agent? ASU researchers transformed the experiment data into useful data stories that shed light on the state of artificial intelligence as a team member.

The dataset that the ASIST project yielded will fuel future research for government, academia and industry alike.

One of the things CHART does really well is taking an interdisciplinary approach. Not only human factors engineering, but also pulling in psychological perspectives and understanding the computational and AI side of things. They’re very willing to embrace innovative methods. The way we pivoted and ended up using a large-scale online game competition was something few academic labs could have accomplished.

— Adam Fouse, principal research engineer and senior director of the Performance Augmentation Systems Division, Aptima