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 demonstration showcases a promising transition of the
latest in multi-agent algorithms into a deployed application.
In particular, it exhibits 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 two 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. 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.