Research on Leaning and Evolutionary Algorithm of Agent for Task Oriented

Guo Feng Xu; Yuan Wen Cai

doi:10.4028/www.scientific.net/AMR.989-994.2033

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 989-994Research on Leaning and Evolutionary Algorithm of...

Research on Leaning and Evolutionary Algorithm of Agent for Task Oriented

Abstract:

The leaning and evolutionary (L&E) algorithm of Agent for task oriented is deeply researched in this paper. Based on the relationship between tasks and the executive Agent, the importance of the research has been elaborated. Moreover, the algorithm is improved by considering the effect of environment and network structure. Reinforcement leaning and complex network have been introduced into the nonlinear genetic algorithm. Finally, some simulations of equipment acquisition tasks are made to test the validity and capability of the algorithm.

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Periodical:

Advanced Materials Research (Volumes 989-994)

Pages:

2033-2036

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.989-994.2033

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Online since:

July 2014

Authors:

Guo Feng Xu*, Yuan Wen Cai

Keywords:

Agent, Evolution, Leaning, Task Oriented

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* - Corresponding Author

References

[1] Macal C M, North M J. Agent-based modeling and simulation[C]/Winter Simulation Conference. Winter Simulation Conference, 2009: 86-98.

DOI: 10.1109/wsc.2009.5429318

Google Scholar

[2] Pisarski S, Rugała A, Byrski A, et al. Evolutionary multi-agent system in hard benchmark continuous optimisation[M]/Applications of Evolutionary Computation. Springer Berlin Heidelberg, 2013: 132-141.

DOI: 10.1007/978-3-642-37192-9_14

Google Scholar

[3] Schmitt L M. Theory of genetic algorithms[J]. Theoretical Computer Science, 2001, 259(1): 1-61.

Google Scholar

[4] Wang L, Tang D. An improved adaptive genetic algorithm based on hormone modulation mechanism for job-shop scheduling problem[J]. Expert Systems with Applications, 2011, 38(6): 7243-7250.

DOI: 10.1016/j.eswa.2010.12.027

Google Scholar

[5] WANG Jun-hong, LIU Bao, YUAN Ruo-quan etc. Design and application of a nonlinear genetic algorithm[J]. Computer simulation, 2010, 27(2): 196-199.

Google Scholar

[6] Grefenstette J J, Moriarty D E, Schultz A C. Evolutionary algorithms for reinforcement learning[J]. arXiv preprint arXiv: 1106. 0221, (2011).

Google Scholar

[7] Strogatz S H. Exploring complex networks[J]. Nature, 2001, 410(6825): 268-276.

DOI: 10.1038/35065725

Google Scholar

[8] HE Da-ren, LIU Zong-hua, WANG Bin-hong. Complex Systems and Complex Networks[M]. Beijing: Higher Education Press, 2009: 188-190.

Google Scholar

[9] QU Wei, ZHENG Shao-yu. Introduction to military equipment acquisition[M]. Beijing: The people's Liberation Army press, (2003).

Google Scholar

[10] North M J, Collier N T, Ozik J, et al. Complex adaptive systems modeling with repast simphony[J]. Complex adaptive systems modeling, 2013, 1(1): 1-26.

DOI: 10.1186/2194-3206-1-3

Google Scholar