Artificial Agents Learn Policies for Multi-Issue Negotiation
用进化计算方法模拟有限理性的人工智能体学习在多议题谈判中达成更优协议，有望用于电子商务系统减少交易损失。
:A well-established body of research consistently shows that people involved in multiple-issue negotiations frequently select Pareto-inferior agreements that "leave money on the table." Using an evolutionary computation approach, we show how simple, boundedly rational, artificial, adaptive agents can learn to negotiate effectively in stylized business negotiations. Furthermore, there is the promise that these agents can be integrated into practicable electronic commerce systems that not only would leave less money on the table, but would enable new types of transactions to be negotiated cost effectively.