TY - GEN
T1 - Maximizing awareness about HIV in social networks of homeless youth with limited information
AU - Yadav, Amulya
AU - Chan, Hau
AU - Jiang, Albert Xin
AU - Xu, Haifeng
AU - Rice, Eric
AU - Tambe, Milind
N1 - Funding Information:
This research was supported by MURI Grant W911NF-11-1-0332 and NIMH Grant number R01-MH093336.
PY - 2017
Y1 - 2017
N2 - 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 deviationtolerant manner. HEALER was deployed in the real world in Spring 2016 with considerable success.
AB - 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 deviationtolerant manner. HEALER was deployed in the real world in Spring 2016 with considerable success.
UR - http://www.scopus.com/inward/record.url?scp=85031903907&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85031903907&partnerID=8YFLogxK
U2 - 10.24963/ijcai.2017/702
DO - 10.24963/ijcai.2017/702
M3 - Conference contribution
AN - SCOPUS:85031903907
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 4959
EP - 4963
BT - 26th International Joint Conference on Artificial Intelligence, IJCAI 2017
A2 - Sierra, Carles
PB - International Joint Conferences on Artificial Intelligence
T2 - 26th International Joint Conference on Artificial Intelligence, IJCAI 2017
Y2 - 19 August 2017 through 25 August 2017
ER -
