Wu Jinghua, Zhang Fujuan, Han Jiali, Li Yan, Sun Yi
School of Management, China University of Mining and Technology (Beijing), Beijing, China.
J Ambient Intell Humaniz Comput. 2022;13(6):2921-2935. doi: 10.1007/s12652-021-03089-w. Epub 2021 Mar 18.
Human-to-agent automated negotiation has many potentials in a variety of applications. How to design an agent with equivalent persuasion capabilities with its human rivals is the key to the success of such systems but the research on this problem is still at its early stage. With the aim of improving agents' persuasion ability, this paper proposes to construct emotional agents and emotion-dependent persuasion actions in automated negotiation with multiple issues. First, a multi-issue evaluation function adjusted by the rival's reputation is constructed to determine whether emotional persuasion is needed. Then, by applying the Weber-Fechner Law, this paper proposes a method to measure an agent's emotion generated by evaluating the rival's proposal. Persuasion is categorized into four types and an emotion-based method is proposed for an agent to select a persuasion type. The selected persuasion type is further related to updating concessions, so that an agent can make concessions adaptive to both the rival's proposal and the focal agent's emotional state. Moreover, a series of numerical experiments on bilateral negotiation between agents are conducted to illustrate the proposed model and validate its effectiveness in improving negotiation efficiency. Theoretical and practical implications as well as limitations are discussed in the end.
人机自动协商在各种应用中具有诸多潜力。如何设计出与人类对手具有同等说服能力的智能体是此类系统成功的关键，但针对该问题的研究仍处于早期阶段。为提高智能体的说服能力，本文提出在多议题自动协商中构建情感智能体和依赖情感的说服行动。首先，构建一个由对手声誉调整的多议题评估函数，以确定是否需要情感说服。然后，应用韦伯 - 费希纳定律，提出一种通过评估对手提议来测量智能体产生的情感的方法。说服被分为四种类型，并为智能体选择说服类型提出了一种基于情感的方法。所选的说服类型进一步与更新让步相关联，以便智能体能够做出既适应对手提议又适应焦点智能体情感状态的让步。此外，进行了一系列关于智能体双边协商的数值实验，以说明所提出的模型并验证其在提高协商效率方面的有效性。最后讨论了理论和实际意义以及局限性。
