Presentation 2015-06-18
A Study on Multiple Sampling and Cooperation Strategy for Stochastic Distributed Constraint Optimization Method
Toshihiro Matsui,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) Distributed Gibbs (DGibbs) is a sampling-based stochastic solution method for Distributed Constraint Optimization Problems (DCOPs), which is a fundamental problem in multiagent system. DGibbs performs a stochastic search on pseudo trees that represent DCOPs. However, the existing method requires relatively large overheads in message communication, since it is a synchronous distributed algorithm. Moreover, the stochastic search has a redundancy. In this study, we investigate the effects and influences of multiple sampling processes, that reduce communication overheads, and a cooperative search strategy among agents.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Multiagent system / Distributed Constraint Optimization Problem / Sampling / Stochastic Search
Paper # AI2015-7
Date of Issue 2015-06-11 (AI)

Conference Information
Committee AI
Conference Date 2015/6/18(1days)
Place (in Japanese) (See Japanese page)
Place (in English)
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair Toshiharu Sugawara(Waseda Univ.)
Vice Chair Tsunenori Mine(Kyushu Univ.) / Daisuke Katagami(Tokyo Polytechnic Univ.)
Secretary Tsunenori Mine(Kyoto Univ.) / Daisuke Katagami(Shizuoka Univ.)
Assistant Yuichi Sei(Univ. of Electro-Comm.)

Paper Information
Registration To Technical Committee on Artificial Intelligence and Knowledge-Based Processing
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A Study on Multiple Sampling and Cooperation Strategy for Stochastic Distributed Constraint Optimization Method
Sub Title (in English)
Keyword(1) Multiagent system
Keyword(2) Distributed Constraint Optimization Problem
Keyword(3) Sampling
Keyword(4) Stochastic Search
1st Author's Name Toshihiro Matsui
1st Author's Affiliation Nagoya Institute of Technology(NITech)
Date 2015-06-18
Paper # AI2015-7
Volume (vol) vol.115
Number (no) AI-97
Page pp.pp.37-42(AI),
#Pages 6
Date of Issue 2015-06-11 (AI)