Improving the Efficiency of Clearing with Multi-agent Teams

We present an anytime algorithm for coordinating multiple autonomous searchers to find a potentially adversarial target on a graphical representation of a physical environment. This problem is closely related to the mathematical problem of searching for an adversary on a graph. Prior methods in the literature treat multi-agent search as either a worst-case problem (i.e., clear an environment of an adversarial evader with potentially infinite speed), or an average-case problem (i.e., minimize average capture time given a model of the target's motion). Both of these problems have been shown to be NP-hard, and optimal solutions typically scale exponentially in the number of searchers. We propose treating search as a resource allocation problem, which leads to a scalable anytime algorithm for generating schedules that clear the environment of a worst-case adversarial target and have good average-case performance considering a nonadversarial motion model. Our algorithm yields theoreti...

• Type: Journal
• Year: 2010
• Where: IJRR
• Authors: Geoffrey Hollinger, Sanjiv Singh, Athanasios Kehagias
• Keywords: Adversarial Target, Anytime Algorithm, Average-case Performance, IJRR 2010