On multiagent online problems with predictions

TL;DR

This paper explores the use of predictive algorithms in multiagent online problems, introducing a two predictor framework to analyze competitive ratios and applying it to a multiagent ski-rental scenario.

Contribution

It introduces a novel two predictor framework for multiagent online algorithms and analyzes their competitive ratios under various predictor accuracy assumptions.

Findings

Optimal algorithms with perfect predictions are not robust to errors.

A more robust algorithm is proposed and benchmarked.

Framework can be applied to other multiagent online problems.

Abstract

We study the power of (competitive) algorithms with predictions in a multiagent setting. We introduce a two predictor framework, that assumes that agents use one predictor for their future (self) behavior, and one for the behavior of the other players. The main problem we are concerned with is understanding what are the best competitive ratios that can be achieved by employing such predictors, under various assumptions on predictor quality. As an illustration of our framework, we introduce and analyze a multiagent version of the ski-rental problem. In this problem agents can collaborate by pooling resources to get a group license for some asset. If the license price is not met then agents have to rent the asset individually for the day at a unit price. Otherwise the license becomes available forever to everyone at no extra cost. In the particular case of perfect other predictions the algorithm that follows the self predictor is optimal but not robust to mispredictions of agent's future behavior; we give an algorithm with better robustness properties and benchmark it.