Quality-bounded Solutions for Finite Bayesian Stackelberg Games: Scaling up


Manish Jain, Milind Tambe, and Christopher Kiekintveld. 2011. “Quality-bounded Solutions for Finite Bayesian Stackelberg Games: Scaling up .” In International Conference on Autonomous Agents and Multiagent Systems.


The fastest known algorithm for solving General Bayesian Stackelberg games with a finite set of follower (adversary) types have seen direct practical use at the LAX airport for over 3 years; and currently, an (albeit non-Bayesian) algorithm for solving these games is also being used for scheduling air marshals on limited sectors of international flights by the US Federal Air Marshals Service. These algorithms find optimal randomized security schedules to allocate limited security resources to protect targets. As we scale up to larger domains, including the full set of flights covered by the Federal Air Marshals, it is critical to develop newer algorithms that scale-up significantly beyond the limits of the current state-of-theart of Bayesian Stackelberg solvers. In this paper, we present a novel technique based on a hierarchical decomposition and branch and bound search over the follower type space, which may be applied to different Stackelberg game solvers. We have applied this technique to different solvers, resulting in: (i) A new exact algorithm called HBGS that is orders of magnitude faster than the best known previous Bayesian solver for general Stackelberg games; (ii) A new exact algorithm called HBSA which extends the fastest known previous security game solver towards the Bayesian case; and (iii) Approximation versions of HBGS and HBSA that show significant improvements over these newer algorithms with only 1- 2% sacrifice in the practical solution quality.
See also: 2011