This work was presented at HAIDM 2014. The 2014 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: This paper proposes the Multi-Operation Patrol Scheduling System (MOPSS), a new system to generate patrols for transit system. MOPSS is based on five contributions. First, MOPSS is the first system to use three fundamentally different adversary models for the threats of fare evasion, terrorism and crime, generating three significantly different types of patrol schedule. Second, to handle uncertain interruptions in the execution of patrol schedules, MOPSS uses Markov decision processes (MDPs) in its scheduling. Third, MOPSS is the first system to account for joint activities between multiple resources, by employing the well known SMART security game model that tackles coordination between defender’s resources. Fourth, we are also the first to deploy a new Opportunistic Security Game model, where the adversary, a criminal, makes opportunistic decisions on when and where to commit crimes. Our fifth, and most important, contribution is the evaluation of MOPSS via real-world deployments, providing data from security games in the field.

Keywords: Security, Game-theory, Real-world deployment.

Citation: Francesco Delle Fave, Matthew Brown, Chao Zhang, Eric Shieh, Albert Jiang and Milind Tambe. Security Games in the Field: Deployments on a Transit System.

Download: http://bit.ly/1TcVx85

This work was presented at HAIDM 2015. The 2015 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: Recently, there has been an increase of interest in domains involving repeated interactions between defenders and adversaries. This has been modeled as a repeated Stackelberg Security Game (repeated SSG). Although different behavioral models have been proposed for the attackers in these games, human subjects experiments for testing these behavioral models in repeated SSGs have not been conducted previously. This paper presents the first “longitudinal study” – at least in the context of SSGs – of testing human behavior models in repeated SSG settings. We provide the following contributions in this paper. First, in order to test the behavioral models, we design a game that simulates the repeated interactions between the defender and the adversary and deploy it on Amazon Mechanical Turk (AMT). Human subjects are asked to participate in this repeated task in rounds of the game, with a break between consecutive rounds. Second, we develop several approaches to keep the human subjects motivated throughout the course of this longitudinal study so that they participate in all measurement occasions, thereby minimizing attrition. We provide results showing improvements of retention rate due to implementation of these approaches. Third, we propose a way of choosing representative payoffs that fit the real-world scenarios as conducting these experiments are extremely time-consuming and we can only conduct a limited number of such experiments.

Keywords: Game Theory, Human Behavior Models, Repeated Stackelberg Games, Longitudinal Experiments, Amazon Mechanical Turk.

Citation: Debarun Kar, Fei Fang, Francesco Delle Fave, Nicole Sintov and Milind Tambe. Conducting Longitudinal Experiments with Behavioral Models in Repeated Stackelberg Security Games on Amazon Mechanical Turk.

Download: http://bit.ly/1rf3wsu