Earticle

초록

영어

In this paper, aiming at the learning deficiency in the given bargaining systems, the decimal code, instead of binary code, is adopted to prevent variables from going beyond limitative scope,
which will cause exceptional strategy. Furthermore, a dynamic bargaining system is presented based on machine learning (MLDBS). The result of experiment shows that the agent in
MLDBS not can only identify its opponents successfully but can change its strategy in term
of different opponents in a bargaining process. It is shown by the experimental datum that MLDBS
increase successful times of bargaining and enhance the average payoff of agent.

목차

Abstract
 1. Introduction
 2. Principle of MLDBS
  2.1 Correlative definition
  2.2 Strategies
  2.3 Operation of bargaining
  2.4 Model of MLDBS
 3. Extracting Charateristics
 4. Genetic Algorithms
  4.1 Method of coding
  4.2 Adaptive function
  4.3 Genetic parameters
 5. BP Neural Network
  5.1 Design of BP neural network
  5.2 Training BP neural network
 6. Adjusting Offer
 7. Simulation
 8. Conclusion
 References

키워드

저자

• Haifeng Guo [ Department of Computer Science and Technology, Yanbian University ]
• Rongyi Cui [ Department of Computer Science and Technology, Yanbian University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국어정보학 [韓國語情報學]

• 간기

  반년간

• pISSN

  2005-1891

• 수록기간

  1999~2019

• 십진분류

  KDC 706 DDC 406