Research on Minimum-Cost-Based Task Decomposition Model of Multiple Expert Systems for Oil-Gas Reservoir Protection Based on Agent

Li Yang; Xie Dong Cao; Jie Li

doi:10.4028/www.scientific.net/AMM.275-277.2650

Paper Titles

Using Interpolation Points to Rectify Cutter Paths of Five-Axis Machining
p.2629

Automatic Side-Cores Construction in Injection Plastic Mold Design for Free-Form NURBS Surface Models
p.2635

Starting Energy Dissipation of Traffic Flow with On-Ramp
p.2640

Robust Quantized Guaranteed Cost Control
p.2646

Research on Minimum-Cost-Based Task Decomposition Model of Multiple Expert Systems for Oil-Gas Reservoir Protection Based on Agent
p.2650

Adaptive Consensus of High Order Multi-Agent Systems with Unknown Nonlinear Dynamics
p.2654

1D and 2D Coupled Numerical Model for Flood Control in Middle Reach of Huaihe River
p.2659

Symmetric Multiwavelet Frames with General Composite Dilation in Higher Dimensions
p.2669

Three Remodeling of the Optical Nerve Head Including Retinal Blood Vessel Based on Live Animal Experiment
p.2673

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 275-277Research on Minimum-Cost-Based Task Decomposition...

Research on Minimum-Cost-Based Task Decomposition Model of Multiple Expert Systems for Oil-Gas Reservoir Protection Based on Agent

Abstract:

To solve collaboration problem in multi-expert system, intelligent agent technology is used in multi-expert system.Firstly,by introducing a minimum-cost-based formal description of task decomposition,a new agent task decompositon model is presented.Secondly, heuristic algorithm for task decomposition is analyzed to solve communication among agents.For illustration, expert systems for oil-gas reservoir protection is utilized to verify the effectiveness of the method.The application of the expert system shows that based-minimum-cost task decompostion model is valid in agent task decomposition and heuristic algorithm for task decomposition can be used as communication among agents.As a result，the proposed cooperation mechanism based on agents can effectively solve the collaboration of experts in the multiple expert systems and improve the accuracy of inference.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 275-277)

Pages:

2650-2653

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.275-277.2650

Citation:

Cite this paper

Online since:

January 2013

Authors:

Li Yang, Xie Dong Cao, Jie Li

Keywords:

Minimum-Cost, Multi-Agent (MA), Multi-Expert, Task Decomposition

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] M. Reina. Applying Expert Systems in Heavy/Extra Heavy Oil Production Operation. SPE 27057. (1994).

DOI: 10.2118/27057-ms

Google Scholar

[2] Espin D.A. Expert System for Selection of Optimum Aritificial Lift Method. SPE 26967. (1994).

Google Scholar

[3] Liao S H. Expert system methodologies and applications—A decade review from 1995 to 2004[J]. Expert Systems with Applications. 2005, 28(1): 93-103.

DOI: 10.1016/j.eswa.2004.08.003

Google Scholar

[4] Shiue W, Li S T, Chen K J. A frame knowledge system for managing financial decision knowledge[J]. Expert Systems with Applications, 2008, 35(3): 1068-1079.

DOI: 10.1016/j.eswa.2007.08.035

Google Scholar

[5] Jahromi M Z, Parvinnia E, John R. A method of learning weighted similarity function to improve the performance of nearest neighbor[J]. Information Sciences, 2009, 179(17): 2964-2973.

DOI: 10.1016/j.ins.2009.04.012

Google Scholar

[6] COLOMBO A W, SCHOOP R, NEUBERT R. An agent-based intelligent control platform for industrial holonic manufacturing systems[J]. IEEE Trans on Industrial Electronics, 2005, 53(1) : 322-337.

DOI: 10.1109/tie.2005.862210

Google Scholar

[7] Zeng G.Z., Yang G.P., Wang X.L. Study of task allocation problem based on agent ability confidence[J]. Chinese Journal of Computers, 2007, 30(11): 1922-(1929).

Google Scholar

[8] S Menon. Effective reformulations for task allocation in distributed systems with a large number of communicating tasks[J]. IEEE Transactions on Knowledge and Data Engineering, 2004, 16(12): 1497-1508.

DOI: 10.1109/tkde.2004.91

Google Scholar