Thursday, November 15, 2018 - 12:00pm
With the maturing of AI and multiagent systems research, we have a tremendous opportunity to direct these advances towards addressing complex societal problems. I will focus on the problems of countering terrorism (for public safety and security), extinction (wildlife conservation), and homelessness (public health in low resource communities). One key multiagent systems challenge that cuts across these problem areas is how to effectively deploy limited intervention resources. In addressing this challenge, we provide advances in multiagent systems research, particularly in terms of computational game theory and agent modeling. For public safety and security, I will introduce the use of Stackelberg security games model for effectively allocating limited security resources. These security games models are used by agencies such as the US Coast Guard and the US Federal Air Marshals Service to assist in the protection of ports, flights and other critical infrastructure. Second, I will illustrate the use of green security games to allocate limited resources in protecting endangered wildlife. By advancing adversary modeling in these games, we have helped removal of snares and arrests of poachers in national parks in Uganda. Third, for public health, I will outline challenges of using limited resources for spreading health information in low resource communities, and algorithms based on games against nature. These algorithms show significant improvements over traditional methods in harnessing social networks to spread HIV-related information among homeless youth. I will also point to directions for future work, illustrating the significant potential of AI for social good.