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Adaptive policies for perimeter surveillance problems

Research output: Contribution to Journal/MagazineJournal articlepeer-review

<mark>Journal publication date</mark>16/05/2020
<mark>Journal</mark>European Journal of Operational Research
Issue number1
Number of pages14
Pages (from-to)265-278
Publication StatusPublished
Early online date6/11/19
<mark>Original language</mark>English


We consider the problem of sequentially choosing observation regions along a line, with an aim of maximising the detection of events of interest. Such a problem may arise when monitoring the movements of endangered or migratory species, detecting crossings of a border, policing activities at sea, and in many other settings. In each case, the key operational challenge is to learn an allocation of surveillance resources which maximises successful detection of events of interest. We present a combinatorial multi-armed bandit model with Poisson rewards and a novel filtered feedback mechanism - arising from the failure to detect certain intrusions - where reward distributions are dependent on the actions selected. Our solution method is an upper confidence bound approach and we derive upper and lower bounds on its expected performance. We prove that the gap between these bounds is of constant order, and demonstrate empirically that our approach is more reliable in simulated problems than competing algorithms.