@conference {1502681,
	title = {Learning Bounded Rationality Models of the Adversary in Repeated Stackelberg Security Games },
	booktitle = {Adaptive and Learning Agents Workshop at the International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2015)},
	year = {2015},
	abstract = {Several competing human behavior models have been proposed
to model and protect against boundedly rational adversaries in repeated Stackelberg security games (SSGs). However, these existing
models fail to address three main issues which are extremely 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,
they assume that sufficient data in the initial rounds will lead to
a reliable model of the adversary. However, our analysis reveals
that the issue is not the amount of data, but that there just is not
enough of the attack surface exposed to the adversary to learn a
reliable model. Third, current leading approaches have failed to include probability weighting functions, even though it is well known
that human beings{\textquoteright} weighting of probability is typically nonlinear.
Moreover, the performances of these models may be critically dependent on the learning algorithm used to learn the parameters of
these models. The first contribution of this paper 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 adaptiveness; (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 comparison of two different approaches for learning the parameters of the bounded rationality models.
Our third contribution is a first {\textquotedblleft}longitudinal study{\textquotedblright} {\textendash} at least in
the context of SSGs {\textendash} of competing models in settings involving
repeated interaction between the attacker and the defender. This
study, where each experiment lasted a period of multiple weeks
with individual sets of human subjects, illustrates the strengths and
weaknesses of different models and shows the advantages of SHARP.},
	author = {Kar, Debarun and Fang, Fei and Delle Fave, Francesco and Sintov, Nicole and Sinha, Arunesh and Galstyan, Aram and An, Bo and Tambe, Milind}
}
