Quality guarantees for region optimal DCOP algorithms

Authors:
Meritxell Vinyals;Eric Shieh;Jesus Cerquides;Juan Antonio Rodriguez-Aguilar;Zhengyu Yin;Milind Tambe;Emma Bowring
Affiliations:
Artificial Intelligence Research Institute (IIIA), Campus UAB, Bellaterra, Spain;University of Southern California, Los Angeles, CA;Artificial Intelligence Research Institute (IIIA), Campus UAB, Bellaterra, Spain;Artificial Intelligence Research Institute (IIIA), Campus UAB, Bellaterra, Spain;University of Southern California, Los Angeles, CA;University of Southern California, Los Angeles, CA;University of the Pacific, Stockton, CA
Venue:
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Year:
2011

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Abstract

k- and t-optimality algorithms [9, 6] provide solutions to DCOPs that are optimal in regions characterized by its size and distance respectively. Moreover, they provide quality guarantees on their solutions. Here we generalise the k- and t-optimal framework to introduce C-optimality, a flexible framework that provides reward-independent quality guarantees for optima in regions characterised by any arbitrary criterion. Therefore, C-optimality allows us to explore the space of criteria (beyond size and distance) looking for those that lead to better solution qualities. We benefit from this larger space of criteria to propose a new criterion, the so-called size-bounded-distance criterion, which outperforms k-and t-optimality.