Abstract
In the strategic facility location problem, a set of agents report their locations in a metric space, and the goal is to use these reports to open a new facility, minimizing an aggregate distance measure from the agents to the facility. However, agents are strategic and may misreport their locations to influence the facility's placement in their favor. The aim is to design truthful mechanisms that ensure agents cannot gain by misreporting. This problem was recently revisited through the learning-augmented framework, aiming to move beyond worst-case analysis by designing truthful mechanisms augmented with (machine-learned) predictions.
In this talk, we will explore this problem in both deterministic and randomized settings, as well as the impact of different types of predictions on the performance of truthful learning-augmented mechanisms.