The Border Patrol Game.

Lead Research Organisation: Lancaster University
Department Name: Mathematics and Statistics

Abstract

There are many reasons we need to defend borders in the modern world. Not only are there the physical borders between countries where we wish to stop illegal trafficking and smuggling, there are the metaphorical borders in cybersecurity and intelligence collection. Whilst in an ideal world we would be able to simultaneously protect the whole border all of the time, due to constraints on budget or other factors it is common to patrol the border focusing on only a small section at a time.
We are using game theory and reinforcement learning techniques to develop strategies with which the defender can use to protect their border. We do this by considering the optimal actions both the smuggler and defender could take, and how they could then play against this.
The project will entail many different aspects of the applied probability and operational research literatures such as: multi-armed bandits, Stackelberg security games and Markov decision processes. We hope to bring these together to solve various problems in this project.

In partnership with Naval Postgraduate School (Monterey, US).

Planned Impact

This proposal will benefit (i) the UK economy and society, (ii) our industrial partners, (iii) the wider community of non-academic employers of doctoral graduates in STOR, (iv) the scientific disciplines of statistics and operational research and associated academic communities, (v) UK doctoral students in STOR, and (vi) the CDT students themselves.

Below we outline how each of these communities will realise these benefits:

(i) The UK economy will gain a competitive edge through a significant increase in the supply and diversity of doctoral STOR professionals with the skills required to undertake influential, responsible and impactful research, and who have been trained to become future leaders. Our goal is that our future alumni who enter industry assume leading roles in realising the major impact that STOR can make in achieving effective data driven decision-making. Our existing alumni are already starting to achieve this. A wider societal benefit will accrue from research contributions to EPSRC Prosperity Outcomes, e.g. to the UK being a Productive and Resilient Nation.

(ii) Our industrial partners will particularly benefit from the skills supply identified in item (i), as likely employers of STOR-i graduates. They will further benefit from teaming with a community of leading edge STOR researchers in the solution of substantive industrial challenges. Mechanisms for the latter include doctoral projects co-supervised with industry, industrial internships, engagement in research clusters and industrial problem-solving days. Our training programme will give students the skills they need to ensure that research is conducted responsibly and that outcomes are successfully communicated to beneficiaries. The value that our industrial partners place on working with STOR-i can be seen through the pledged cash support of £1.7M.

(iii) A wider benefit will accrue from the employment of STOR-i graduates, equipped as described in items (i) and (ii), across non-partner public and private sector organisations. The breadth and depth of training provided by the CDT will enable students to quickly make a difference in these organisations, using their research skills to affect significant change.

(iv) The STOR academic community will benefit from methodological advances and from the increase and diversity in the supply of STOR researchers who value, and have experience of, collaborative research. Our alumni will be leaders in 21st Century Statistics with a strong culture of, and training in, reproducible research and a focus on achieving impact with excellence. Our recruitment strategy will further benefit this community in achieving a healthier supply of high-quality doctoral candidates from diverse backgrounds. Our research internship programme gives top mathematically able individuals from across the UK an experience of STOR research and has been shown to increase applications for STOR PhD programmes across the UK.

(v) Elements of the STOR-i programme will benefit the wider community of UK doctoral students in STOR. Using financial support from our industrial partners, we will continue our National Associate Scheme. This will provide up to 50 UK STOR doctoral students with funding and access to elements of STOR-i's training programme. An annual conference will provide opportunities for learning, networking and sharing research progress to members of the scheme.

(vi) STOR-i students will benefit from a personalised programme that will support each individual in fully achieving their research leadership potential, whether in academia or industry. Students will be given the tools and opportunities to develop research and broader skills that will enable them to achieve maximum scientific impact for their work. Our current alumni provide strong evidence that these future graduates will be extremely employable.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S022252/1 01/10/2019 31/03/2028
2284168 Studentship EP/S022252/1 01/10/2019 31/03/2024 Matthew Darlington
 
Description We are developing new algorithms in order to look at the behaviour of smugglers and patrollers in a border control environment. Our work will expand on previous work by firstly considering more complex models for the players, and secondly develop more efficient ways to computationally analyse the problem. We are looking to discover results which are interesting to practitioners in the border patrol environment, as well as making interesting mathematical analysis in game theory. Currently, this is still ongoing work and so the results have not been fully compiled yet, however, we are making good progress towards this.
Exploitation Route The outcomes could be used by operators in a defence scenario in order to look at how they should act. Secondly, someone needing to solve stochastic games of a particular structure might use our algorithms in a different scenario.
Sectors Aerospace, Defence and Marine,Education