Collaborating Authors


AAAI Conferences

In this paper, we propose a fractional preference model for the facility location game with two facilities that serve the similar purpose on a line where each agent has his location information as well as fractional preference to indicate how well they prefer the facilities. The preference for each facility is in the range of [0, L] such that the sum of the preference for all facilities is equal to 1. The utility is measured by subtracting the sum of the cost of both facilities from the total length L where the cost of facilities is defined as the multiplication of the fractional preference and the distance between the agent and the facilities. We first show that the lower bound for the objective of minimizing total cost is at least Ω(n 1/3). Hence, we use the utility function to analyze the agents' satification.

Computing Optimal Mixed Strategies for Security Games with Dynamic Payoffs

AAAI Conferences

Security agencies in the real world often need to protect targets with time-dependent values, e.g., tourist sites where the number of travelers changes over time. Since the values of different targets often change asynchronously, the defender can relocate security resources among targets dynamically to make the best use of limited resources. We propose a game-theoretic scheme to develop dynamic, randomized security strategies in consideration of adversary's surveillance capability. This differs from previous studies on security games by considering varying target values and continuous strategy spaces of the security agency and the adversary. The main challenge lies in the computational intensiveness due to the continuous, hence infinite strategy spaces. We propose an optimal algorithm and an arbitrarily near-optimal algorithm to compute security strategies under different conditions. Experimental results show that both algorithms significantly outperform existing approaches.

Optimal Machine Strategies to Commit to in Two-Person Repeated Games

AAAI Conferences

The problem of computing optimal strategy to commit to in various games has attracted intense research interests and has important real-world applications such as security (attacker-defender) games. In this paper, we consider the problem of computing optimal leader’s machine to commit to in two-person repeated game, where the follower also plays a machine strategy. Machine strategy is the generalized notion of automaton strategy, where the number of states in the automaton can be possibly infinite. We begin with the simple case where both players are confined to automata strategies, and then extend to general (possibly randomized) machine strategies. We first give a concise linear program to compute the optimal leader’s strategy and give two efficient implementations of the linear program: one via enumeration of a convex hull and the other via randomization. We then investigate the case where two machines have different levels of intelligence in the sense that one machine is able to record more history information than the other. We show that an intellectually superior leader, sometimes considered being exploited by the follower, can figure out the follower’s machine by brute-force and exploit the follower in return.

onathan Gratch and Gerald

AAAI Conferences

In machine learning there is considerable interest in techniques which improve planning ability. Initial investigations have identified a wide variety of techniques to address this issue. Progress has been hampered by the utilityproblem, a basic tradeoff between the benefit of learned knowledge and the cost to locate and apply relevant knowledge. In this paper we describe the COMPOSER system which embodies a probabilistic solution to the utility problem.

Security Games with Information Leakage: Modeling and Computation

AAAI Conferences

Most models of Stackelberg security games assume that the attacker only knows the defender's mixed strategy, but is not able to observe (even partially) the instantiated pure strategy. Such partial observation of the deployed pure strategy -- an issue we refer to as information leakage -- is a significant concern in practical applications. While previous research on patrolling games has considered the attacker's real-time surveillance, our settings, therefore models and techniques, are fundamentally different. More specifically, after describing the information leakage model, we start with an LP formulation to compute the defender's optimal strategy in the presence of leakage. Perhaps surprisingly, we show that a key subproblem to solve this LP (more precisely, the defender oracle) is NP-hard even for the simplest of security game models. We then approach the problem from three possible directions: efficient algorithms for restricted cases, approximation algorithms, and heuristic algorithms for sampling that improves upon the status quo. Our experiments confirm the necessity of handling information leakage and the advantage of our algorithms.