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CSL Researcher Tamer Başar and his team of collaborators received a $7.5 million Department of Defense Multidisciplinary University Research Initiative (MURI) award to form a better understanding of how teams of humans and machines make decisions and develop more reliable and secure multi-layer networks where team interactions take place.
The project emphasizes the study of communication and information acquisition and exchange between agents of different types in an adversarial environment, such as the battlefield. It is expected to have a dramatic impact on the most critical issues of inference, decision-making and overall situational awareness.
"Our national security and economic health depends on our ability to provide robust, timely and accurate responses to challenges that arise in complex networked environments where humans and machines with varying capabilities and intents interact. Our work addresses that need, and is expected to have a dramatic impact on the most critical issues of inference, decision making, and overall situational awareness,” said Başar, Swanlund Chair and a professor of electrical and computer engineering.
In addition to Başar, CSL researchers Geir Dullerud, Negar Kiyavash, Cedric Langbort and R. Srikant, along with six faculty from Georgia Tech, Stanford, UC-Berkeley and The University of Maryland, are also participating. The professors come from a variety of backgrounds, including engineering (electrical, computer, industrial, mechanical and aerospace), computer science and economics.
Air Force Office of Scientific Research will fund this project for five years. In total, the DoD funded 32 MURI projects out of the 411 white papers originally submitted to the program back in December.
Langbort, an assistant professor of aerospace engineering, says that in a modern battlefield decisions are made by multiple agents in different locations. These agents can be humans or machines and share information through networks. This introduces new vulnerabilities, and new ways for adversaries to be strategic.
“Instead of fighting directly on the battlefield, they can just fight at the network level by either physically breaking into it or, in a more covert but still dangerous way, strategically modifying the information it carries,” Langbort says.
The network attacks could be multifarious, including cognitive jamming, data tampering, malicious gossiping, disruption of physical links and servers, and hacking. They can also be stealthy, like a timing attack.
“The goal of this project is to understand how these strategic disruptions impact decision making and to architect the network, information flow and decision algorithms themselves so that vulnerability to adversarial acts is minimized,” Başar says. “For that, it is important to model the assumptions that agents make about each other and the adversary, and humans and machines build such assumptions very differently.”
The team is using the framework of game theory, which is concerned with adversaries whose goals are non-aligned and who are competing with each other.
Dullerud, a professor of mechanical engineering, added that the team will examine the many levels of game theory, asking questions such as: What are the exact ways in which large numbers of both people and machines interact? What information do they share and how accurate is it? Are they telling the truth?
“The problem with this sort of interaction network is that there are too many entities, and it is not possible to model them all exactly. An interesting aspect is that if you look more closely at what appears to be a single modeling entity, it may well be a simplification of a game that is being played out on a smaller spatial or temporal scale, so one really has a game of games,” Dullerud said.
To gain a better understanding of computer and human interaction, Dullerud is developing a distributed robotics testbed using a network of hovercraft and other autonomous vehicles that can interact with both human and machine-based decision makers. “We’d like to have a cyberphysical network comprised of humans and machines to help us experimentally determine what we need to know in order to systematically predict the behavior of a potentially large-scale human-machine network.”
Dullerud believes the CSL’s multidisciplinary history will contribute to the success of the project. “Also, education-wise, this is a very innovative project that will open up many opportunities for both graduate and undergraduate research training,” he said.