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We propose a novel framework for integrating Hawkes processes withmulti-armed bandit algorithms to solve spatio-temporal event forecasting anddetection problems when data may be undersampled or spatially biased. Inparticular, we introduce an upper confidence bound algorithm using Bayesianspatial Hawkes process estimation for balancing the tradeoff between exploitinggeographic regions where data has been collected and exploring geographicregions where data is unobserved. We first validate our model using simulateddata and then apply it to the problem of disaster search and rescue using callsfor service data from hurricane Harvey in 2017. Our model outperforms the stateof the art baseline spatial MAB algorithms in terms of cumulative reward andseveral other ranking evaluation metrics.

Hawkes Process Multi-armed Bandits for Disaster Search and Rescue