论文标题
霍克斯处理多军匪徒，用于灾难搜索和救援
Hawkes Process Multi-armed Bandits for Disaster Search and Rescue
论文作者
论文摘要
我们提出了一个新的框架，用于将鹰派工艺与多臂Bandit算法整合在一起，以求解当数据被删除或空间偏置的数据时，可以解决时空事件预测和检测问题。特别是，我们使用贝叶斯空间鹰队过程估算介绍了一种上限置信度结合算法，以平衡利用数据收集数据的地理区域与探索数据未观察到数据的地理区域之间的权衡。我们首先使用模拟数据验证了我们的模型，然后使用2017年Harvey Harvey的呼吁将其应用于灾难搜索和救援问题。我们的模型在累积奖励和其他几个排名评估指标方面优于最先进的基线空间MAB算法。
We propose a novel framework for integrating Hawkes processes with multi-armed bandit algorithms to solve spatio-temporal event forecasting and detection problems when data may be undersampled or spatially biased. In particular, we introduce an upper confidence bound algorithm using Bayesian spatial Hawkes process estimation for balancing the tradeoff between exploiting geographic regions where data has been collected and exploring geographic regions where data is unobserved. We first validate our model using simulated data and then apply it to the problem of disaster search and rescue using calls for service data from hurricane Harvey in 2017. Our model outperforms the state of the art baseline spatial MAB algorithms in terms of cumulative reward and several other ranking evaluation metrics.
