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Optimal interdiction of urban criminals with the aid of real-time information

Optimal interdiction of urban criminals with the aid of real-time information
Optimal interdiction of urban criminals with the aid of real-time information
Most violent crimes happen in urban and suburban cities. With emerging tracking techniques, law enforcement officers can have real-time location information of the escaping criminals and dynamically adjust the security resource allocation to interdict them. Unfortunately, existing work on urban network security games largely ignores such information. This paper addresses this omission. First, we show that ignoring the real-time information can cause an arbitrarily large loss of efficiency. To mitigate this loss, we propose a novel NEtwork purSuiT game (NEST) model that captures the interaction between an escaping adversary and a defender with multiple resources and real-time information available. Second, solving NEST is proven to be NP-hard. Third, after transforming the non-convex program of solving NEST to a linear program, we propose our incremental strategy generation algorithm, including: (i) novel pruning techniques in our best response oracle; and (ii) novel techniques for mapping strategies between subgames and adding multiple best response strategies at one iteration to solve extremely large problems. Finally, extensive experiments show the effectiveness of our approach, which scales up to realistic problem sizes with hundreds of nodes on networks including the real network of Manhattan.
AAAI
Zhang, Youzhi
c5a29f2c-a55e-4cca-9fb2-2ff168050dfd
Guo, Qingyu
9922ab2c-9e8f-484f-ae29-0455d5edc6b3
An, Bo
4b0743f9-91c9-4452-868c-1d12b4e9f456
Tran-Thanh, Long
e0666669-d34b-460e-950d-e8b139fab16c
Jennings, Nicholas R.
3f6b53c2-4b6d-4b9d-bb51-774898f6f136
Zhang, Youzhi
c5a29f2c-a55e-4cca-9fb2-2ff168050dfd
Guo, Qingyu
9922ab2c-9e8f-484f-ae29-0455d5edc6b3
An, Bo
4b0743f9-91c9-4452-868c-1d12b4e9f456
Tran-Thanh, Long
e0666669-d34b-460e-950d-e8b139fab16c
Jennings, Nicholas R.
3f6b53c2-4b6d-4b9d-bb51-774898f6f136

Zhang, Youzhi, Guo, Qingyu, An, Bo, Tran-Thanh, Long and Jennings, Nicholas R. (2019) Optimal interdiction of urban criminals with the aid of real-time information. In 33rd AAAI Conference on Artificial Intelligence. AAAI. 8 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

Most violent crimes happen in urban and suburban cities. With emerging tracking techniques, law enforcement officers can have real-time location information of the escaping criminals and dynamically adjust the security resource allocation to interdict them. Unfortunately, existing work on urban network security games largely ignores such information. This paper addresses this omission. First, we show that ignoring the real-time information can cause an arbitrarily large loss of efficiency. To mitigate this loss, we propose a novel NEtwork purSuiT game (NEST) model that captures the interaction between an escaping adversary and a defender with multiple resources and real-time information available. Second, solving NEST is proven to be NP-hard. Third, after transforming the non-convex program of solving NEST to a linear program, we propose our incremental strategy generation algorithm, including: (i) novel pruning techniques in our best response oracle; and (ii) novel techniques for mapping strategies between subgames and adding multiple best response strategies at one iteration to solve extremely large problems. Finally, extensive experiments show the effectiveness of our approach, which scales up to realistic problem sizes with hundreds of nodes on networks including the real network of Manhattan.

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Published date: 1 February 2019

Identifiers

Local EPrints ID: 431274
URI: https://eprints.soton.ac.uk/id/eprint/431274
PURE UUID: c9efa87c-6a33-4c2f-a5b2-54b257ec5434
ORCID for Long Tran-Thanh: ORCID iD orcid.org/0000-0003-1617-8316

Catalogue record

Date deposited: 29 May 2019 16:30
Last modified: 30 Nov 2019 01:34

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Contributors

Author: Youzhi Zhang
Author: Qingyu Guo
Author: Bo An
Author: Long Tran-Thanh ORCID iD
Author: Nicholas R. Jennings

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