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AAMAS Conference 2012 Conference Paper

Computing Optimal Strategy against Quantal Response in Security Games

• Rong Yang
• Fernando Ord
• oacute;
• ntilde; ez
• Milind Tambe

To step beyond the first-generation deployments of attacker-defender security games -- for LAX Police, US FAMS and others -- it is critical that we relax the assumption of perfect rationality of the human adversary. Indeed, this assumption is a well-accepted limitation of classical game theory and modeling human adversaries' bounded rationality is critical. To this end, quantal response (QR) has provided very promising results to model human bounded rationality. However, in computing optimal defender strategies in real-world security games against a QR model of attackers, we face difficulties including (1) solving a nonlinear non-convex optimization problem efficiently for massive real-world security games; and (2) addressing constraints on assigning security resources, which adds to the complexity of computing the optimal defender strategy. This paper presents two new algorithms to address these difficulties: \textsc{GOSAQ} can compute the globally optimal defender strategy against a QR model of attackers when there are no resource constraints and gives an efficient heuristic otherwise; \textsc{PASAQ} in turn provides an efficient approximation of the optimal defender strategy with or without resource constraints. These two novel algorithms are based on three key ideas: (i) use of a binary search method to solve the fractional optimization problem efficiently, (ii) construction of a convex optimization problem through a non-linear transformation, (iii) building a piecewise linear approximation of the non-linear terms in the problem. Additional contributions of this paper include proofs of approximation bounds, detailed experimental results showing the advantages of extsc{GOSAQ} and \textsc{PASAQ} in solution quality over the benchmark algorithm (\textsc{BRQR}) and the efficiency of \textsc{PASAQ}. Given these results, \textsc{PASAQ} is at the heart of the PROTECT system, which is deployed for the US Coast Guard in the port of Boston, and is now headed to other ports.

AAMAS Conference 2012 Conference Paper

Multi-Objective Optimization for Security Games

• Matthew Brown
• Bo An
• Christopher Kiekintveld
• Fernando Ord
• oacute;
• ntilde; ez
• Milind Tambe

The burgeoning area of security games has focused on real-world domains where security agencies protect critical infrastructure from a diverse set of adaptive adversaries. There are security domains where the payoffs for preventing the different types of adversaries may take different forms (seized money, reduced crime, saved lives, etc) which are not readily comparable. Thus, it can be difficult to know how to weigh the different payoffs when deciding on a security strategy. To address the challenges of these domains, we propose a fundamentally different solution concept, multi-objective security games (MOSG), which combines security games and multi-objective optimization. Instead of a single optimal solution, MOSGs have a set of Pareto optimal (non-dominated) solutions referred to as the Pareto frontier. The Pareto frontier can be generated by solving a sequence of constrained single-objective optimization problems (CSOP), where one objective is selected to be maximized while lower bounds are specified for the other objectives. Our contributions include: (i) an algorithm, Iterative $\epsilon$-Constraints, for generating the sequence of CSOPs; (ii) an exact approach for solving an MILP formulation of a CSOP (which also applies to multi-objective optimization in more general Stackelberg games); (iii) heuristics that achieve speedup by exploiting the structure of security games to further constrain a CSOP; (iv) an approximate approach for solving an algorithmic formulation of a CSOP, increasing the scalability of our approach with quality guarantees. Additional contributions of this paper include proofs on the level of approximation and detailed experimental evaluation of the proposed approaches.

IJCAI Conference 2011 Conference Paper

Human Behavior Analysis from Video Data Using Bag-of-Gestures

• V
• iacute; ctor Ponce
• Mario Gorga
• Xavier Bar
• oacute;
• Sergio Escalera

Human Behavior Analysis in Uncontrolled Environmentscan be categorized in two main challenges: 1) Feature extraction and 2) Behavior analysisfrom a set of corporal language vocabulary. Inthis work, we present our achievements characterizingsome simple behaviors from visual data ondifferent real applications and discuss our plan forfuture work: low level vocabulary definition frombag-of-gesture units and high level modelling andinference of human behaviors.

IJCAI Conference 2011 Conference Paper

Semi-Supervised Learning from a Translation Model between Data Distributions

• Henry Anaya-S
• aacute; nchez
• Jos
• eacute; Mart
• iacute; nez-Sotoca
• Adolfo Mart
• iacute; nez-Us
• oacute;

In this paper, we introduce a probabilistic classification model to address the task of semi-supervised learning. The major novelty of our proposal stems from measuring distributional relationships between the labeled and unlabeled data. This is achieved from a stochastic translation model between data distributions that is estimated from a mixture model. The proposed classifier is defined from the combination of both the translation model and a kernel logistic regression on labeled data. Experimental results obtained over synthetic and real-world data sets validate the usefulness of our proposal.

AAMAS Conference 2008 Conference Paper

Deployed ARMOR Protection: The Application of a Game Theoretic Model for Security at the Los Angeles International Airport

• James Pita
• Manish Jain
• Janusz Marecki
• Fernando Ord
• oacute;
• ntilde; ez
• Christopher Portway
• Milind Tambe

Security at major locations of economic or political importance is a key concern around the world, particularly given the threat of terrorism. Limited security resources prevent full security coverage at all times, which allows adversaries to observe and exploit patterns in selective patrolling or monitoring, e. g. they can plan an attack avoiding existing patrols. Hence, randomized patrolling or monitoring is important, but randomization must provide distinct weights to different actions based on their complex costs and benefits. To this end, this paper describes a promising transition of the latest in multi-agent algorithms – in fact, an algorithm that represents a culmination of research presented at AAMAS – into a deployed application. In particular, it describes a software assistant agent called ARMOR (Assistant for Randomized Monitoring over Routes) that casts this patrolling/monitoring problem as a Bayesian Stackelberg game, allowing the agent to appropriately weigh the different actions in randomization, as well as uncertainty over adversary types. ARMOR combines three key features: (i) It uses the fastest known solver for Bayesian Stackelberg games called DOBSS, where the dominant mixed strategies enable randomization; (ii) Its mixed-initiative based interface allows users to occasionally adjust or override the automated schedule based on their local constraints; (iii) It alerts the users if mixed-initiative overrides appear to degrade the overall desired randomization. ARMOR has been successfully deployed since August 2007 at the Los Angeles International Airport (LAX) to randomize checkpoints on the roadways entering the airport and canine patrol routes within the airport terminals. This paper examines the information, design choices, challenges, and evaluation that went into designing ARMOR.

IJCAI Conference 2007 Conference Paper

• Istv
• aacute; n B
• iacute; r
• oacute;
• Zolt
• aacute; n Szamonek
• Csaba Szepesv
• aacute; ri

When data is scarce or the alphabet is large, smoothing the probability estimates becomes inescapable when estimating n-gram models. In this paper we propose a method that implements a form of smoothing by exploiting similarity information of the alphabet elements. The idea is to view the log-conditional probability function as a smooth function defined over the similarity graph. The algorithm that we propose uses the eigenvectors of the similarity graph as the basis of the expansion of the log conditional probability function whose coefficients are found by solving a regularized logistic regression problem. The experimental results demonstrate the superiority of the method when the similarity graph contains relevant information, whilst the method still remains competitive with state-of-the-art smoothing methods even in the lack of such information.

IJCAI Conference 2007 Conference Paper

• Andr
• aacute; s Gy
• ouml; rgy
• Levente Kocsis
• Ivett Szab
• oacute;
• Csaba Szepesv
• aacute; ri

In this paper we consider an extension of the multi-armed bandit problem. In this generalized setting, the decision maker receives some side information, performs an action chosen from a finite set and then receives a reward. Unlike in the standard bandit settings, performing an action takes a random period of time. The environment is assumed to be stationary, stochastic and memoryless. The goal is to maximize the average reward received in one unit time, that is, to maximize the average rate of return. We consider the on-line learning problem where the decision maker initially does not know anything about the environment but must learn about it by trial and error. We propose an "upper confidence bound"-style algorithm that exploits the structure of the problem. We show that the regret of this algorithm relative to the optimal algorithm that has perfect knowledge about the problem grows at the optimal logarithmic rate in the number of decisions and scales polynomially with the parameters of the problem.