NDLI: Improving Asynchronous Search for Distributed Generalized Assignment Problem

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Improving Asynchronous Search for Distributed Generalized Assignment Problem

Content Provider	ACM Digital Library
Author	Sun, Tingting Xu, Yang He, Qingyi
Abstract	Distributed Generalized Assignment Problem (D-GAP) is very popular in scalable multi-agent systems. However, existing algorithms are not effective or efficient in large scale or highly dynamic domains due to the limited communication and computation resource. In this paper, we present a novel approach to address this issue. To reduce communication, we propose a decentralized model for agents to jointly search for optimized solutions. Considering the complexity of D-GAP in a massive multi-agent system, agents cannot perform the optimal search based on their local views, we propose a heuristic algorithm. By inferring knowledge from their previous communicated searches, agents are able to predict how to deploy their future similar searches more efficiently. If an agent can solve some parts of D-GAP well, similar searches will be sent to it. By taking the advantage of the accumulation effect to agents' local knowledge, agents can independently make simple decisions with highly reliable performance and limited communication overheads. Finally, we present a simulation to demonstrate the feasibility and efficiency of our algorithm.
Starting Page	38
Ending Page	42
Page Count	5
File Format	PDF
ISBN	9780769548807
DOI	10.1109/WI-IAT.2012.142
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-12-04
Access Restriction	Subscribed
Subject Keyword	Heterogeneous multi-agent system d-gap decentralized heuristic algorithm coordination
Content Type	Text
Resource Type	Article

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