Earticle

초록

영어

Decision-making process refers in a particular situation, and choosing from several existing optional strategy evaluation process of certain strategy, however, in a complex environment, individual decisions tend to be affected by other individuals and random change of strategy, thus it is difficult to make a best decision. Although game theory provides a variety of decision-making plan, but it is not clear how the decision is influenced by the experience. The best decision-making behaviors need flexible adjustment strategy accord to the recent gains. In order to explore the nature of the human decision-making process USES the zero-sum game COINS of the positive and negative game, assuming that flexibility was produced in the process of reinforcement learning and a reinforcement learning model is established. In addition, the entire record the event related potential in the process of the subjects in the experiment. Analysis of ERP (Event Related Potential) data mainly focus on feedback related negative wave, which was considered to reflect the error signal and benefit related to the brain potential. Results show that after the loss to the other party of the event related potential amplitude suggested that participants would change the policy of pushing in the subsequent regulations, and the result was satisfied.

목차

Abstract
 1. Introduction
 2. Game Theory
  2.1. Reinforcement Learning Model
 3. Feature Extraction [4-10]
 4. Results
 5. Discussion
 Acknowledgements
 References

키워드

저자

• Yu Zhou [ School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou 450011, China, Key Laboratory of Innovation Method and Decision Management System of Guangdong Province, Guangzhou Guangdong, 510641, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605