Maximizing Awareness about HIV in Social Networks of Homeless Youth with Limited Information

Maximizing Awareness about HIV in Social Networks of Homeless Youth with Limited Information

Amulya Yadav, Hau Chan, Albert Xin Jiang, Haifeng Xu, Eric Rice, Milind Tambe

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Best Sister Conferences. Pages 4959-4963. https://doi.org/10.24963/ijcai.2017/702

This paper presents HEALER, a software agent that recommends sequential intervention plans for use by homeless shelters, who organize these interventions to raise awareness about HIV among homeless youth. HEALER's sequential plans (built using knowledge of social networks of homeless youth) choose intervention participants strategically to maximize influence spread, while reasoning about uncertainties in the network. While previous work presents influence maximizing techniques to choose intervention participants, they do not address two real-world issues: (i) they completely fail to scale up to real-world sizes; and (ii) they do not handle deviations in execution of intervention plans. HEALER handles these issues via two major contributions: (i) HEALER casts this influence maximization problem as a POMDP and solves it using a novel planner which scales up to previously unsolvable real-world sizes; and (ii) HEALER allows shelter officials to modify its recommendations, and updates its future plans in a deviation-tolerant manner. HEALER was deployed in the real world in Spring 2016 with considerable success.
Keywords:
Artificial Intelligence: multi-agent systems
Artificial Intelligence: automated planning
Artificial Intelligence: artificial intelligence