University of Southern California
Research Group
AI for Social Good: A multiagent systems perspective

By: Milind Tambe, Fei Fang, Bryan Wilder, and Eugene Vorobeychik

A tutorial at AAMAS 2018, July 10, 2018, Stockholm, Sweden


Discussions about the future negative consequences of AI sometimes drown out discussions of the future potential of AI in helping us solve complex societal problems. This tutorial will focus in contrast on the immediate positives that could be achieved with AI, i.e., "AI For Social Good", focusing in particular on the use of multiagent research towards this goal.



The main topics of this tutorial will be:
  • Public safety and security: We will give an overview on the challenge of multiagent systems for public safety and security, specifically for effective security resource allocation in domains ranging from counter-terrorism to cybersecurity. We will briefly discuss our security games software that has been used by agencies such as the US Coast Guard, the US Federal Air Marshals Service, and other law enforcement agencies internationally, and outline some of the more recent research focus on cybersecurity.

  • Wildlife conservation: We will introduce how multiagent systems can be used to protect forests, fish and wildlife. We will discuss machine learning and game theory based solution approaches for predictive and prescriptive analysis. We will cover tests in a national park in Uganda that have led to removal of snares and arrests of poachers, potentially saving endangered animals.

  • Social intervention: We will introduce the use of multiagent research to confront social issues and assist low-resource communities. Societies worldwide face enormous challenges: preventing and treating disease, shifting to renewable energy sources, confronting poverty, and many more. We will introduce algorithmic techniques for targeting or enhancing interventions in such domains, focusing on three technical subjects: social networks, epidemiology, and agent-based simulation. As a case study, we will discuss field tests of influence maximization algorithms for HIV prevention among homeless youth in Los Angeles.


Milind Tambe is Helen N. and Emmett H. Jones Professor in Engineering at the University of Southern California (USC) and the Founding Co-Director of CAIS, the USC Center for Artificial Intelligence in Society, where his research focuses on "AI for Social Good". He is a fellow of AAAI and ACM, as well as recipient of the ACM/SIGAI Autonomous Agents Research Award, Christopher Columbus Fellowship Foundation Homeland security award, INFORMS Wagner prize in Operations Research, Rist Prize of the Military Operations Research Society, IBM Faculty Award, Okawa foundation award, RoboCup scientific challenge award, and other awards including the Orange County Engineering Council Outstanding Project Achievement Award, USC Associates award for creativity in research and USC Viterbi use-inspired research award.


Fei Fang is Assistant Professor in the School of Computer Science at Carnegie Mellon University. Dr. Fang’s research lies in the field of artificial intelligence and multi-agent systems, focusing on data-aware game theory and mechanism design with applications to security, sustainability, and mobility domains. Her dissertation is selected as the runner-up for IFAAMAS-16 Victor Lesser Distinguished Dissertation Award, and is selected to be the winner of the William F. Ballhaus, Jr. Prize for Excellence in Graduate Engineering Research as well as the Best Dissertation Award in Computer Science at the University of Southern California. Her work has won the Innovative Application Award at Innovative Applications of Artificial Intelligence (IAAI’16), the Outstanding Paper Award in Computational Sustainability Track at the International Joint Conferences on Artificial Intelligence (IJCAI’15). Her work has been deployed by the US Coast Guard for protecting the Staten Island Ferry in New York City since April 2013. Her work has led to the deployment of PAWS (Protection Assistant for Wildlife Security) in multiple conservation areas around the world, which provides predictive and prescriptive analysis for anti-poaching effort.


Bryan Wilder is a PhD student at the University of Southern California, where he is advised by Milind Tambe. His research focuses on algorithmic social intervention: computational approaches for optimally targeting social and behavioral interventions in domains such as public health and homelessness. For instance, his algorithms have been deployed by Los Angeles area homeless shelters to prevent HIV spread. He is supported by a National Science Foundation Graduate Research Fellowship and his work was nominated for a best paper award at AAMAS 2017.


Yevgeniy Vorobeychik is an Assistant Professor of Computer Science, Computer Engineering, and Biomedical Informatics at Vanderbilt University. Previously, he was a Principal Research Scientist at Sandia National Laboratories. Between 2008 and 2010 he was a post-doctoral research associate at the University of Pennsylvania Computer and Information Science department. He received Ph.D. (2008) and M.S.E. (2004) degrees in Computer Science and Engineering from the University of Michigan, and a B.S. degree in Computer Engineering from Northwestern University. His work focuses on game theoretic modeling of security and privacy, adversarial machine learning, algorithmic and behavioral game theory and incentive design, optimization, agent-based modeling, complex systems, network science, and epidemic control. Dr. Vorobeychik received an NSF CAREER award in 2017, and was invited to give an IJCAI-16 early career spotlight talk. He was nominated for the 2008 ACM Doctoral Dissertation Award and received honorable mention for the 2008 IFAAMAS Distinguished Dissertation Award.