Conquering Adversary Behavioral Uncertainty in Security Games: An Efficient Modeling Robust based Algorithm


2016. “Conquering Adversary Behavioral Uncertainty in Security Games: An Efficient Modeling Robust based Algorithm .” In 30th AAAI Conference on Artificial Intelligence (AAAI) (Student Abstract).


Real-world deployed applications of Stackelberg Security Games (Shieh et al. 2012; Basilico, Gatti, and Amigoni 2009; Letchford and Vorobeychik 2011) have led to significant research emphasis on modeling the attacker’s bounded rationality (Yang et al. 2011; Nguyen et al. 2013). One key assumption in behavioral modeling is the availability of a significant amount of data to obtain an accurate prediction. However, in real-world security domains such as the wildlife protection, this assumption may be inapplicable due to the limited access to real-world data (Lemieux 2014), leading to uncertainty in the attacker’s behaviors — a key research challenge of security problems. Recent research has focused on addressing uncertainty in behavioral modeling, following two different approaches: 1) one approach assumes a known distribution of multiple attacker types, each follows a certain behavioral model, and attempts to solve the resulting Bayesian games (Yang et al. 2014); and 2) another considers the existence of multiple attacker types of which behavioral models are perfectly known, but without a known distribution over the types. It then only considers the worst attacker type for the defender (Brown, Haskell, and Tambe 2014). These two approaches have several limitations. First, both still require a sufficient amount of data to precisely estimate either the distribution over attacker types (the former approach) or the model parameters for each individual type (the latter approach). Second, solving the resulting Bayesian games in the former case is computationally expensive. Third, the latter approach tends to be overly conservative as it only focuses on the worst-case attacker type. This paper remedies these shortcomings of state-of-theart approaches when addressing behavioral uncertainty in SSG by providing three key contributions. First, we present a new game model with uncertainty in which we consider a single behavioral model to capture decision making of the whole attacker population (instead of multiple behavioral models); uncertainty intervals are integrated with the chosen model to capture behavioral uncertainty. The idea of uncertainty interval is commonly used in literature (Aghassi and Bertsimas 2006) and has been shown to effectively represent uncertainty in SSG (Kiekintveld, Islam, and Kreinovich 2013). Second, based on this game model, we propose a new efficient robust algorithm that computes the defender’s optimal strategy which is robust to the uncertainty. Overall, the resulting robust optimization problem for computing the defender’s optimal strategy against the worst case of behavioral uncertainty is a non-linear non-convex fractional maximin problem. Our algorithm efficiently solves this problem based on the following key insights: 1) it converts the problem into a single maximization problem via a non-linear conversion for fractional terms and the dual of the inner minimization in maximin; 2) a binary search is then applied to remove the fractional terms; and 3) the algorithm explores extreme points of the feasible solution region and uses a piece-wise linear approximation to convert the problem into a Mixed Integer Linear Program (MILP). Our new algorithm provides an O(+ 1 K )-optimal solution where is the convergence threshold for the binary search and K is the number of segments in the piecewise approximation.
See also: 2016