Facing the challenge of human-agent negotiations via effective general opponent modeling
Facing the challenge of human-agent negotiations via effective general opponent modeling
复制标题
通过有效的通用对手建模应对人类与代理谈判的挑战
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
Sarit Kraus
中科院分区：
文献类型：
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作者：
Yinon Oshrat;R. Lin;Sarit Kraus
能够有效地与人谈判的自动谈判代理必须处理这样一个事实，即人们的行为是不同的，每个人都可能以不同的方式进行谈判。因此，自动化代理必须依赖于一个良好的对手建模组件来建模他们的对手，并使他们的行为适应他们的合作伙伴。在本文中，我们提出了KBAgent。KBAgent是一个自动化的谈判者，只与每个人谈判一次，并使用其他人过去的谈判会话作为一般对手建模的知识库。该数据库用于提取对方接受的可能性和可能提出的建议。与人进行的实验表明，KBAgent有效地与人进行谈判，甚至实现更好的效用值比另一个自动谈判，是有效的谈判与人。此外，KBAgent在个人效用方面比扮演相同角色的人类同行达成了更好的协议。
Automated negotiation agents capable of negotiating efficiently with people must deal with the fact that people are diverse in their behavior and each individual might negotiate in a different manner. Thus, automated agents must rely on a good opponent modeling component to model their counterpart and adapt their behavior to their partner. In this paper we present the KBAgent. The KBAgent is an automated negotiator that negotiates with each person only once, and uses past negotiation sessions of others as a knowledge base for general opponent modeling. The database is used to extract the likelihood of acceptance and proposals that may be offered by the opposite side. Experiments conducted with people show that the KBAgent negotiates efficiently with people and even achieves better utility values than another automated negotiator, shown to be efficient in negotiations with people. Moreover, the KBAgent achieves significantly better agreements, in terms of individual utility, than the human counterparts playing the same role.