text:   Approach",10,"['Glad to share our recent UAI-2021 paper @UncertaintyInAI , titled ""Contingency-Aware Influence Maximization: A Reinforcement Learning Approach."" We propose to address the contingency-aware influence maximization (CAIM) problem from a RL perspective. <LINK>', ""CAIM is motivated by a series of studies in our group with @MilindTambe_AI, on HIV prevention among homeless youth social networks in the city of LA. Different from normal IM problems, there is uncertainty in a node's willingness to be seeds. (image credit: Wilder et al.) https://t.co/5VIzEGKb5r"", ""Despite recent success in the field using POMDPs or greedy algorithms (Yadav et al. 2016, Wilder et al. 2018, 2021), a main limitation of scaling their methods to more homeless youth shelters is the large run time, a big burden to NGOs who don't have HPC resources."", ""Inspired by recent work that uses RL to address combinatorial optimization problems, we extend it to the CAIM problem. We show the extension is computationally hard due to the uncertainty of a node's willingness, and propose a new reward shaping technique."", 'The reward shaping component is proven to be computationally efficient both in theory (via the submodularity property of the influence cascade models) and in empirical evaluation (via an ablation study).', 'Main takeaway -- our algorithm RL4IM achieves comparable influence to SOTA algorithm for CAIM, while requiring negligible runtime during test phase, which can be directly used by NGOs - a result making RL4IM an ideal alternative for CAIM in the low-resource computing paradigm.', 'Our work is an example of how RL can be used for social good work, and we hope to encourage more AI researchers in exploring the idea here to assist AI for social good in the low-resource computing domain.', 'Join work with @WeiQiu9, @HanChingOu, Bo An, and @MilindTambe_AI', 'Recent works on HIV prevention among homeless youth networks using influence maximization, with success in field test, by @brwilder @AmulyaYadav19 @MilindTambe_AI  @EricRicePhD', 'Work that combines graph representation learning and reinforcement learning to address combinatorial optimization problems, by @lyeskhalil et al. 2017']",21,06,2139