Handling Continuous Space Security Games with Neural Networks


Nitin Kamra, Fei Fang, Debarun Kar, Yan Liu, and Milind Tambe. 2017. “Handling Continuous Space Security Games with Neural Networks.” In In IWAISe-17: 1st International Workshop on A.I. in Security held at the International Joint Conference on Artificial Intelligence.


Despite significant research in Security Games, limited efforts have been made to handle game domains with continuous space. Addressing such limitations, in this paper we propose: (i) a continuous space security game model that considers infinitesize action spaces for players; (ii) OptGradFP, a novel and general algorithm that searches for the optimal defender strategy in a parametrized search space; (iii) OptGradFP-NN, a convolutional neural network based implementation of OptGradFP for continuous space security games; (iv) experiments and analysis with OptGradFP-NN. This is the first time that neural networks have been used for security games, and it shows the promise of applying deep learning to complex security games which previous approaches fail to handle.
See also: Conservation, 2017
Last updated on 07/26/2021