@conference {1502029,
	title = {Comparing Human Behavior Models in Stackelberg Security Games: An Extended Study },
	booktitle = {Artificial Intelligence Journal (AIJ), Elsevier, DOI},
	year = {2016},
	abstract = {Several competing human behavior models have been proposed to model boundedly
rational adversaries in repeated Stackelberg Security Games (SSG). However, these existing models fail to address three main issues which are detrimental to defender
performance. First, while they attempt to learn adversary behavior models from adversaries{\textquoteright} past actions ({\textquotedblleft}attacks on targets{\textquotedblright}), they fail to take into account adversaries{\textquoteright}
future adaptation based on successes or failures of these past actions. Second, existing
algorithms fail to learn a reliable model of the adversary unless there exists sufficient
data collected by exposing enough of the attack surface {\textemdash} a situation that often arises
in initial rounds of the repeated SSG. Third, current leading models have failed to include probability weighting functions, even though it is well known that human beings{\textquoteright}
weighting of probability is typically nonlinear.
To address these limitations of existing models, this article provides three main
contributions. Our first contribution is a new human behavior model, SHARP, which
mitigates these three limitations as follows: (i) SHARP reasons based on success or
failure of the adversary{\textquoteright}s past actions on exposed portions of the attack surface to
model adversary adaptivity; (ii) SHARP reasons about similarity between exposed and
unexposed areas of the attack surface, and also incorporates a discounting parameter to
mitigate adversary{\textquoteright}s lack of exposure to enough of the attack surface; and (iii) SHARP
integrates a non-linear probability weighting function to capture the adversary{\textquoteright}s true
weighting of probability. Our second contribution is a first {\textquotedblleft}repeated measures study{\textquotedblright}
{\textendash} at least in the context of SSGs {\textendash} of competing human behavior models. This study,
where each experiment lasted a period of multiple weeks with individual sets of human subjects on the Amazon Mechanical Turk platform, illustrates the strengths and
weaknesses of different models and shows the advantages of SHARP. Our third major
contribution is to demonstrate SHARP{\textquoteright}s superiority by conducting real-world human
subjects experiments at the Bukit Barisan Seletan National Park in Indonesia against
wildlife security experts.},
	url = {http://dx.doi.org/10.1016/j.artint.2016.08.002},
	author = {Kar, Debarun and Fang, Fei and Francesco M. Delle Fave and Sintov, Nicole and Tambe, Milind and Lyet, Arnaud}
}
