Abstract
The Distributed Constraint Optimization Problem (DCOP) has been studied as a fundamental optimization problem that represents various problems on multiagent systems. We focus on the asymmetric DCOPs where each objective function is differently defined as an evaluation of an agent. This class of problems is studied as a multi-objective problem for the preferences of individual agents. In this work, we investigate the possibility of a solution framework based on relaxation methods as a scalable and inexact solution approach for this class of problems. We address a bottleneck problem that minimizes the worst-case cost value. As the first study, we apply a penalty method to the minimization problems of the maximum cost values.
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Acknowledgement
This work was supported in part by JSPS KAKENHI Grant Number JP16K00301 and Tatematsu Zaidan.
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Matsui, T., Matsuo, H. (2018). A Study of Relaxation Approaches for Asymmetric Constraint Optimization Problems. In: Miller, T., Oren, N., Sakurai, Y., Noda, I., Savarimuthu, B.T.R., Cao Son, T. (eds) PRIMA 2018: Principles and Practice of Multi-Agent Systems. PRIMA 2018. Lecture Notes in Computer Science(), vol 11224. Springer, Cham. https://doi.org/10.1007/978-3-030-03098-8_39
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DOI: https://doi.org/10.1007/978-3-030-03098-8_39
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