Keeping pace with criminals: an extended study of designing patrol allocation against adaptive opportunistic criminals

DOI10.3390/g7030015zbMath1406.91066OpenAlexW2470568209MaRDI QIDQ725011

Manish Jain, Milind Tambe, Arunesh Sinha, Debarun Kar, Chao Zhang, Ripple Goyal, Shahrzad Gholami

Publication date: 1 August 2018

Published in: Games (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3390/g7030015

zbMATH Keywords

optimizationgame theorysecurity games

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Stochastic scheduling theory in operations research (90B36) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20) Rationality and learning in game theory (91A26) Mathematical sociology (including anthropology) (91D99)

Uses Software

• PRMLT

Cites Work

• Unnamed Item
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• Occam's razor
• Defending Against Opportunistic Criminals: New Game-Theoretic Frameworks and Algorithms
• Security and Game Theory
• A STATISTICAL MODEL OF CRIMINAL BEHAVIOR
• The generalized distributive law

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