When Players Affect Target Values: Modeling and Solving Dynamic Partially Observable Security Games


Xinrun Wang, Milind Tambe, Branislav Boˇsansky ́, and Bo An. 2019. “When Players Affect Target Values: Modeling and Solving Dynamic Partially Observable Security Games.” In . Conference on Decision and Game Theory for Security, 2019.


Most of the current security models assume that the values of targets/areas are static or the changes (if any) are scheduled and
known to the defender. Unfortunately, such models are not sufficient
for many domains, where actions of the players modify the values of
the targets. Examples include wildlife scenarios, where the attacker can
increase value of targets by secretly building supporting facilities. To address such security game domains with player-affected values, we first
propose DPOS3G, a novel partially observable stochastic Stackelberg
game where target values are determined by the players’ actions; the defender can only partially observe these targets’ values, while the attacker
can fully observe the targets’ values and the defender’s strategy. Second,
we propose RITA (Reduced game Iterative Transfer Algorithm), which
is based on the heuristic search value iteration algorithm for partially observable stochastic game (PG-HSVI) and introduces three key novelties:
(a) building a reduced game with only key states (derived from partitioning the state space) to reduce the numbers of states and transitions
considered when solving the game; (b) incrementally adding defender’s
actions to further reduce the number of transitions of the game; (c) providing novel heuristics for lower bound initialization of the algorithm.
Third, we conduct extensive experimental evaluations of the algorithms
and the results show that RITA significantly outperforms the baseline
PG-HSVI algorithm on scalability while allowing for trade off in scalability and solution quality.
See also: 2019
Last updated on 04/08/2020