@conference {1502314,
	title = {Robust Strategy against Unknown Risk-averse Attackers in Security Games },
	booktitle = {International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2015)},
	year = {2015},
	abstract = {Stackelberg security games (SSGs) are now established as a powerful tool in security domains. In this paper, we consider a new
dimension of security games: the risk preferences of the attacker.
Previous work assumes a risk-neutral attacker that maximizes his
expected reward. However, extensive studies show that the attackers in some domains are in fact risk-averse, e.g., terrorist groups
in counter-terrorism domains. The failure to incorporate the risk
aversion in SSG models may lead the defender to suffer significant
losses. Additionally, defenders are uncertain about the degree of
attacker{\textquoteright}s risk aversion. Motivated by this challenge this paper provides the following five contributions: (i) we propose a novel model
for security games against risk-averse attackers with uncertainty in
the degree of their risk aversion; (ii) we develop an intuitive MIBLP formulation based on previous security games research, but
find that it finds locally optimal solutions and is unable to scale up;
(iii) based on insights from our MIBLP formulation, we develop
our scalable BeRRA algorithm that finds globally ǫ-optimal solutions; (iv) our BeRRA algorithm can also be extended to handle
other risk-aware attackers, e.g., risk-seeking attackers; (v) we show
that we do not need to consider attacker{\textquoteright}s risk attitude in zero-sum
games.},
	author = {Qian, Yundi and William B. Haskell and Tambe, Milind}
}
