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Don’t put all your strategies in one basket: Playing green security games with imperfect prior knowledge

Don’t put all your strategies in one basket: Playing green security games with imperfect prior knowledge
Don’t put all your strategies in one basket: Playing green security games with imperfect prior knowledge
Security efforts for wildlife monitoring and protection of endangered species (e.g., elephants, rhinos, etc.) are constrained by limited resources available to law enforcement agencies. Recent progress in Green Security Games (GSGs) has led to patrol planning algorithms for strategic allocation of limited patrollers to deter adversaries in environmental settings. Unfortunately, previous approaches to these problems suffer from several limitations. Most notably, (i) previous work in GSG literature relies on exploitation of error-prone machine learning (ML) models of poachers’ behavior trained on (spatially) biased historical data; and (ii) online learning approaches for repeated security games (similar to GSGs) do not account for spatio-temporal scheduling constraints while planning patrols, potentially causing significant shortcomings in the effectiveness of the planned patrols. Thus, this paper makes the following novel contributions: (I) We propose MINION-sm, a novel online learning algorithm for GSGs which does not rely on any prior error-prone model of attacker behavior, instead, it builds an implicit model of the attacker on-the-fly while simultaneously generating scheduling constraint-aware patrols. MINION-sm achieves a sublinear regret against an optimal hindsight patrol strategy. (II) We also propose MINION, a hybrid approach where our MINION-sm model and an ML model (based on historical data) are considered as two patrol planning experts and we obtain a balance between them based on their observed empirical performance. (III) We show that our online learning algorithms significantly outperform existing state-of-the-art solvers for GSGs.
395-403
International Foundation for Autonomous Agents and Multiagent Systems
Gholami, Shahrzad
b0a9d25c-1011-4957-a78a-e3d036fca570
Yadav, Amulya
d7f79289-4ad1-4f32-9491-938d21a5141d
Tran-Thanh, Long
e0666669-d34b-460e-950d-e8b139fab16c
Dilkina, Bistra
19ef92ea-bcba-4e9a-bf4a-855edbc44da2
Tambe, Milind
a620fda8-c4fe-4193-a396-fe6de595fc6f
Agmon, N.
Taylor, M.E.
Elkind, E.
Veloso, M.
Gholami, Shahrzad
b0a9d25c-1011-4957-a78a-e3d036fca570
Yadav, Amulya
d7f79289-4ad1-4f32-9491-938d21a5141d
Tran-Thanh, Long
e0666669-d34b-460e-950d-e8b139fab16c
Dilkina, Bistra
19ef92ea-bcba-4e9a-bf4a-855edbc44da2
Tambe, Milind
a620fda8-c4fe-4193-a396-fe6de595fc6f
Agmon, N.
Taylor, M.E.
Elkind, E.
Veloso, M.

Gholami, Shahrzad, Yadav, Amulya, Tran-Thanh, Long, Dilkina, Bistra and Tambe, Milind (2019) Don’t put all your strategies in one basket: Playing green security games with imperfect prior knowledge. Agmon, N., Taylor, M.E., Elkind, E. and Veloso, M. (eds.) In Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems. International Foundation for Autonomous Agents and Multiagent Systems. pp. 395-403 .

Record type: Conference or Workshop Item (Paper)

Abstract

Security efforts for wildlife monitoring and protection of endangered species (e.g., elephants, rhinos, etc.) are constrained by limited resources available to law enforcement agencies. Recent progress in Green Security Games (GSGs) has led to patrol planning algorithms for strategic allocation of limited patrollers to deter adversaries in environmental settings. Unfortunately, previous approaches to these problems suffer from several limitations. Most notably, (i) previous work in GSG literature relies on exploitation of error-prone machine learning (ML) models of poachers’ behavior trained on (spatially) biased historical data; and (ii) online learning approaches for repeated security games (similar to GSGs) do not account for spatio-temporal scheduling constraints while planning patrols, potentially causing significant shortcomings in the effectiveness of the planned patrols. Thus, this paper makes the following novel contributions: (I) We propose MINION-sm, a novel online learning algorithm for GSGs which does not rely on any prior error-prone model of attacker behavior, instead, it builds an implicit model of the attacker on-the-fly while simultaneously generating scheduling constraint-aware patrols. MINION-sm achieves a sublinear regret against an optimal hindsight patrol strategy. (II) We also propose MINION, a hybrid approach where our MINION-sm model and an ML model (based on historical data) are considered as two patrol planning experts and we obtain a balance between them based on their observed empirical performance. (III) We show that our online learning algorithms significantly outperform existing state-of-the-art solvers for GSGs.

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Published date: 17 May 2019

Identifiers

Local EPrints ID: 431272
URI: https://eprints.soton.ac.uk/id/eprint/431272
PURE UUID: 427d8cbc-5ff7-497f-bda7-59932b76dcb4
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 May 2019 00:33

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Contributors

Author: Shahrzad Gholami
Author: Amulya Yadav
Author: Long Tran-Thanh ORCID iD
Author: Bistra Dilkina
Author: Milind Tambe
Editor: N. Agmon
Editor: M.E. Taylor
Editor: E. Elkind
Editor: M. Veloso

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