Homeless youth are prone to human immun-odeficiency virus (HIV) due to their engagement in high-risk behavior such as unprotected sex, sex under influence of drugs, and so on. Many nonproft agencies conduct interventions to educate and train a select group of homeless youth about HIV prevention and treatment practices and rely on word-of-mouth spread of information through their one single social network Previous work in strategic selection of intervention participants does not handle uncertainties in the social networks' structure and evolving network state, potentially causing significant shortcomings in spread of information. Thus, we developed PSINET, a decision-support system to aid the agencies in this task. PSINET includes the following key novelties: (1) it handles uncertainties in network structure and evolving network state; (2) it addresses these uncertainties by using POMDPs in influence maximization; and (3) it provides algorithmic advances to allow high-quality approximate solutions for such POMDPs. Simulations show that PSINET achieves around 60 percent more information spread over the current state of the art. PSINET was developed in collaboration with My Friend's Place (a drop-in agency serving homeless youth in Los Angeles) and is currently being reviewed by its officials.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence