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
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
저자정보
참고문헌
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