Mining frequent agent action patterns for effective multi-agent-based web service composition

Authors:
Xiaofeng Wang;Wenjia Niu;Gang Li;Xinghua Yang;Zhongzhi Shi
Affiliations:
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, P.R. China;Institute of Acoustics, Chinese Academy of Sciences, Beijing, P.R. China;School of Information Technology, Deakin University, Vic, Australia;Institute of Acoustics, Chinese Academy of Sciences, Beijing, P.R. China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, P.R. China
Venue:
ADMI'11 Proceedings of the 7th international conference on Agents and Data Mining Interaction
Year:
2011

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Abstract

The dynamic description logic (DDL ) is utilized as one emerging AI planning -related solution for automatic Web service composition. However, reasoning utilization when facing the real world service applications in such DDL -related solutions is still an open problem. In this paper, we propose the cooperative reasoning-based multi-agent model (CREMA ) which can systematically incorporate DDL action reasoning with data mining, together with a support -based planning method for task decomposition in order to improve the overall throughput of the Web service execution. The case study and experimental analysis demonstrates the capability of the proposed approach.