Optimal Discrete Search with a Map.

有效的搜索策略在现实世界的各种情况下都是必要的。马来西亚航空公司370航班的搜索失败花费了2亿多澳元。因此，重要的是要了解，如果未来发生类似的情况，如何以最有效的方式使用如此大量的资金。不仅搜索的代价非常昂贵，而且没有及时找到搜索目标的风险甚至会更昂贵。例如，在自然灾害发生后，救援队越早找到失踪者，失踪者的生存机会就越大。经典搜索问题假设搜索目标隐藏在多个不同位置中的一个，并且在搜索正确位置时存在已知的发现概率。在这种情况下，搜索地点的最佳顺序可以通过将搜索过程建模为一个多手强盗来找到。这是一个经过充分研究的数学模型，其灵感来自老虎机，在老虎机中，玩家需要做出一系列决定来最大化某些奖励。在现有文献中，大多数搜索模型假设这些位置可以在瞬间之间移动，并且没有额外的成本。这种假设极大地简化了问题，但在许多实际应用程序中并不成立。在这个博士学位的过程中，我们的目标是开发和评估搜索策略的有效性，包括在不同地点之间旅行的时间或经济成本。与海军研究生院（美国蒙特雷）合作。

Effective search strategies are necessary in a wide range of real-world situations. The unsuccessful search for Malaysian Airlines flight 370 cost more than two hundred million Australian dollars. It is therefore important to understand how such large amounts of money can be used in the most efficient way possible were a similar even to happen in the future. Not only can the act of searching be very expensive but the risk of not finding the target of the search in time can be even more costly. For example, the earlier a rescue squad can find a missing person after a natural disaster the greater that person's chances of survival.The classical search problem assumes that the target of the search is hidden in one of multiple distinct locations and that when searching the correct location there is a known probability of discovery. In this case, the best possible order to search the locations can be found by modelling the search process as a multi-armed bandit. This is a well-studied mathematical model inspired by slot machines where a series of decisions are made to maximise some reward. In the existing literature, most search models assume that these locations can be moved between instantaneously and at no additional cost. This assumption massively simplifies the problem but doesn't hold in many real-world applications. Over the course of this PhD, our aim is to develop and evaluate the effectiveness of search strategies that incorporate the time or financial costs of traveling between locations.In partnership with Naval Postgraduate School (Monterey, US).