IFAAMAS Influential Paper Award

Nineteenth International Conference on Autonomous Agents and Multi-Agent Systems
9-13 May 2020, Auckland, New Zealand

The 2020 IFAAMAS Influential paper award committee has recommended the following two papers for the 2020 award:

A. Procaccia and M. Tennenholtz, 2009,  “Approximate mechanism design without money”, Proceedings of the 10th ACM Conference on Electronic Commerce (ACM EC 2009), pp. 177-186

This paper was the first to formally initiate the field of approximate mechanism design without money, as the title accurately suggests. It blends a key contribution from economics (mechanism design without money) and a key contribution from computer science (approximation algorithms), thus bonding the two disciplines further. Its publication has led to an explosion of papers on mechanism design without money. The concept has been applied to a vast number of fundamental problems such as facility location, resource allocation/cake-cutting, scheduling, assignment problem, matching/kidney exchange, voting, classification, auctions without money, and automated mechanism design. The extended version of the paper, appearing in the ACM Transactions of Economics and Computation in 2013, received the distinction of the ACM Computing Reviews “Best of 2013”.

K. Dresner and P. Stone, 2008, “A multiagent  approach to autonomous intersection management” Journal of Artificial Intelligence, Vol 31, pp. 591-656.

This paper is an important work that set a new direction in the research of transportation systems for autonomous vehicles. Looking ahead to the time when autonomous cars will be common, Dresner and Stone proposed a new intersection control protocol called Autonomous Intersection Management (AIM) and showed that by leveraging the control and network capabilities of autonomous vehicles it is possible to design an intersection control protocol that is much more efficient than traffic signals. The AIM protocol allows more vehicles to simultaneously cross an intersection, thus effectively reducing the delay of vehicles by orders of magnitude compared to traffic signals. What is surprising is that Dresner and Stone found that AIM can reduce the traffic delay at intersections to almost zero at most traffic levels—a discovery that has a great impact on transportation research. This promising result triggered a series of studies of traffic management for autonomous vehicles along the research direction set by AIM.