Using abstractions to solve opportunistic crime security games at scale

Chao Zhang, Victor Bucarey, Ayan Mukhopadhyay, Arunesh Sinha, Yundi Qian, Yevgeniy Vorobeychik, Milind Tambe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we aim to deter urban crime by recommending optimal police patrol strategies against opportunistic criminals in large scale urban problems. While previous work has tried to learn criminals' behavior from real world data and generate patrol strategies against opportunistic crimes, it cannot scale up to large-scale urban problems. Our first contribution is a game abstraction framework that can handle opportunistic crimes in large-scale urban areas. In this game abstraction framework, we model the interaction between officers and opportunistic criminals as a game with discrete targets. By merging similar targets, we obtain an abstract game with fewer total targets. We use real world data to learn and plan against opportunistic criminals in this abstract game, and then propagate the results of this abstract game back to the original game. Our second contribution is the layer-generating algorithm used to merge targets as described in the framework above. This algorithm applies a mixed integer linear program (MILP) to merge similar and geographically neighboring targets in the large scale problem. As our third contribution, we propose a planning algorithm that recommends a mixed strategy against opportunistic criminals. Finally, our fourth contribution is a heuristic propagation model to handle the problem of limited data we occasionally encounter in large-scale problems. As part of our collaboration with local police departments, we apply our model in two large scale urban problems: a university campus and a city. Our approach provides high prediction accuracy in the real datasets; furthermore, we project significant crime rate reduction using our planning strategy compared to current police strategy.

Original languageAmerican English
Title of host publicationAAMAS 2016 - Proceedings of the 2016 International Conference on Autonomous Agents and Multiagent Systems
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages196-204
Number of pages9
ISBN (Electronic)9781450342391
StatePublished - 2016
Externally publishedYes
Event15th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2016 - Singapore, Singapore
Duration: May 9 2016May 13 2016

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS

Conference

Conference15th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2016
Country/TerritorySingapore
CitySingapore
Period5/9/165/13/16

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering

Keywords

  • Abstraction
  • Machine learning
  • Security games

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