Combining online learning and equilibrium
computation in security games
Richard Klima, Viliam Lisy, and Christopher Kiekintveld
In Conference On Decision and Game Theory for Security (GameSec). 2015.
This is the author's version of the work.
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Abstract
Game-theoretic analysis has emerged as an important method
for making resource allocation decisions in both infrastructure protection
and cyber security domains. However, static equilibrium models de ned
based on inputs from domain experts have weaknesses; they can be inaccurate,
and they do not adapt over time as the situation (and adversary)
evolves. In cases where there are frequent interactions with an attacker,
using learning to adapt to an adversary revealed behavior may lead to
better solutions in the long run. However, learning approaches need a
lot of data, may perform poorly at the start, and may not be able to
take advantage of expert analysis. We explore ways to combine equilibrium
analysis with online learning methods with the goal of gaining the
advantages of both approaches. We present several hybrid methods that
combine these techniques in di erent ways, and empirically evaluated the
performance of these methods in a game that models a border patrolling
scenario.
