PSINET - An Online POMDP Solver for HIV Prevention in Homeless Populations

Homeless youth are prone to Human Immunodeficiency Virus (HIV) due to their engagement in high risk behavior such as unprotected sex, sex under influence of drugs, etc. Many non-profit 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 social network. Previous work in strategic selection of intervention participants does not handle uncertainties in the social network’s 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: (i) it handles uncertainties in network structure and evolving network state; (ii) it addresses these uncertainties by using POMDPs in influence maximization; and (iii) it provides algorithmic advances to allow high quality approximate solutions for such POMDPs. Simulations show that PSINET achieves 60% 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 their officials.

[1]  Jean-Charles Pomerol,et al.  Multicriterion Decision in Management: Principles and Practice , 2012 .

[2]  Wheeler Ruml,et al.  Anticipatory On-Line Planning , 2012, ICAPS.

[3]  Arun G. Chandrasekhar,et al.  The Diffusion of Microfinance , 2012, Science.

[4]  Reinhard Diestel,et al.  Graph Theory , 1997 .

[5]  Malte Helmert,et al.  Trial-Based Heuristic Tree Search for Finite Horizon MDPs , 2013, ICAPS.

[6]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[7]  N. Milburn,et al.  Mobilizing homeless youth for HIV prevention: a social network analysis of the acceptability of a face-to-face and online social networking intervention. , 2012, Health education research.

[8]  E. Rice,et al.  How should network-based prevention for homeless youth be implemented? , 2013, Addiction.

[9]  Patrik Haslum,et al.  Plan Quality Optimisation via Block Decomposition , 2013, IJCAI.

[10]  N. Milburn,et al.  Position-specific HIV risk in a large network of homeless youths. , 2012, American journal of public health.

[11]  Andreas Krause,et al.  Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization , 2010, J. Artif. Intell. Res..

[12]  E. Rice.  The Positive Role of Social Networks and Social Networking Technology in the Condom-Using Behaviors of Homeless Young People , 2010, Public health reports.

[13]  Tamara G. Kolda,et al.  Community structure and scale-free collections of Erdös-Rényi graphs , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  Joel Veness,et al.  Monte-Carlo Planning in Large POMDPs , 2010, NIPS.

[15]  Andreas Krause,et al.  Cost-effective outbreak detection in networks , 2007, KDD '07.

[16]  David Hsu,et al.  DESPOT: Online POMDP Planning with Regularization , 2013, NIPS.

[17]  Wei Chen,et al.  Scalable influence maximization for prevalent viral marketing in large-scale social networks , 2010, KDD.

[18]  Leslie Pack Kaelbling,et al.  Learning Policies for Partially Observable Environments: Scaling Up , 1997, ICML.

[19]  R. Winett,et al.  Randomised, controlled, community-level HIV-prevention intervention for sexual-risk behaviour among homosexual men in US cities , 1997, The Lancet.

[20]  Martin L. Puterman,et al.  Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .

[21]  Masahiro Kimura,et al.  Tractable Models for Information Diffusion in Social Networks , 2006, PKDD.

[22]  Leandro Soriano Marcolino,et al.  Diverse Randomized Agents Vote to Win , 2014, NIPS.