Automated Negotiation Strategies Prediction in Multi-Agent Systems

被引：0
|
作者：
Sirbu, Lavinia-Stefania [1 ]
Radu, Serban [1 ]
机构：
[1] Univ Politehn Bucuresti, Comp Sci Dept, Bucharest, Romania
来源：
2017 19TH INTERNATIONAL SYMPOSIUM ON SYMBOLIC AND NUMERIC ALGORITHMS FOR SCIENTIFIC COMPUTING (SYNASC 2017) | 2017年
关键词：
multi-agent systems; automated negotiation; negotiation strategy; machine learning;
D O I：
10.1109/SYNASC.2017.00048
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
The paper presents different negotiation strategies needed for reaching a mutually acceptable agreement between providers and consumers that are modeled as software agents. The focus is on designing, implementing and evaluating different complex strategies, based on the negotiation context and multiple communication protocols between the agents. For the strategies to become successful, it is essential for the agent to possess information about the other parties and the characteristics of the market. Learning is a defining characteristic for an intelligent behavior, so, with the help of machine learning algorithms, an agent builds an advanced model, using the gathered information from previous actions.
引用
收藏
页码：255 / 262
页数：8
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