Dynamic Incentive Mechanism Design for COVID-19 Social Distancing
Xuan Rong Zane Ho, Wei Yang Bryan Lim, Hongchao Jiang, Jer Shyuan Ng, Han Yu, Zehui Xiong, Dusit Niyato, Chunyan Miao
[AAAI-22] Demonstrations
Abstract:
As countries enter the endemic phase of COVID-19, people's risk of exposure to the virus is greater than ever. There is a need to make more informed decisions in our daily lives on avoiding crowded places. Crowd monitoring systems typically require costly infrastructure. We propose a crowd-sourced crowd monitoring platform which leverages user inputs to generate crowd counts and forecast location crowdedness. A key challenge for crowd-sourcing is a lack of incentive for users to contribute. We propose a Reinforcement Learning based dynamic incentive mechanism to optimally allocate rewards to encourage user participation.
Sessions where this paper appears
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Poster Session 3
Blue 6
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Poster Session 7
Blue 6
