A Negotiation Model of Autonomous Agent with LearningMechanism for Semi-Conductor Testing Scheduling

• Main Authors: Chao-Chieh Hsu, 許朝傑
• Other Authors: Kung-Jeng Wang
• Format: Others
• Language: zh-TW
• Published: 2003
• Online Access: http://ndltd.ncl.edu.tw/handle/72862812943045457487

碩士 === 中原大學 === 工業工程研究所 === 91 === Automated negotiation is an active research domain in dynamic games. This study focuses on an one-to-one automated negotiation model, and develops five different server agents. The five different agents are Random Agent, Rational Agent, Cooperative Agent with trade-offs, Cooperative Agent and Learning Agent. A random agent will propose a feasible schedule to its client. A rational agent generates a negotiation proposal using negotiation decision function (NDF). And generates a proposal by a generic algorithm (GA) in which its score is not lower than the score of negotiation proposal. A cooperative agent with trade-offs generates a negotiation proposal by NDF and the trade-offs mechanism. A naive cooperative agent also generates a negotiation proposal by NDF, and generates an available scheduling by GA which seeking the maximize utility of the other agent. Finally, a learning agent will predict the weights of negotiation issues of its opponent by perceptron neural network. This study applied the proposal method to the semiconductor testing scheduling and its experimental outcomes showed a promising results.