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Negotiated Collusion: Modeling Social Language and its Relationship Effects in Intelligent Agents

机译：协商共谋：在智能代理中建模社会语言及其关系效果

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摘要

Building a collaborative trusting relationship with users is crucial in a wide range of applications, such as advice-giving or financial transactions, and some minimal degree of cooperativeness is required in all applications to even initiate and maintain an interaction with a user. Despite the importance of this aspect of human-human relationships, few intelligent systems have tried to build user models of trust, credibility, or other similar interpersonal variables, or to influence these variables during interaction with users. Humans use a variety of kinds of social language, including small talk, to establish collaborative trusting interpersonal relationships. We argue that such strategies can also be used by intelligent agents, and that embodied conversational agents are ideally suited for this task given the myriad multimodal cues available to them for managing conversation. In this article we describe a model of the relationship between social language and interpersonal relationships, a new kind of discourse planner that is capable of generating social language to achieve interpersonal goals, and an actual implementation in an embodied conversational agent. We discuss an evaluation of our system in which the use of social language was demonstrated to have a significant effect on users' perceptions of the agent's knowledgableness and ability to engage users, and on their trust, credibility, and how well they felt the system knew them, for users manifesting particular personality traits.
机译：与用户建立协作信任关系在诸如建议或财务交易之类的广泛应用程序中至关重要，并且在所有应用程序中甚至都需要某种程度的协作才能启动和维持与用户的交互。尽管人与人之间关系的这一方面很重要，但很少有智能系统尝试建立信任，信誉或其他类似人际变量的用户模型，或在与用户交互时影响这些变量。人类使用包括闲聊在内的多种社交语言来建立协作式信任人际关系。我们认为，此类策略也可以由智能代理使用，并且考虑到他们可用于管理对话的多种多模式提示，因此具体化的对话代理非常适合此任务。在本文中，我们描述了社交语言和人际关系之间的关系的模型，能够生成社交语言以实现人际目标的新型话语计划器，以及在具体化的对话代理中的实际实现。我们讨论了对我们的系统的评估，在该评估中，社交语言的使用被证明对用户对代理的了解程度和与用户互动能力的感知，对他们的信任，信誉以及他们对系统的了解程度有很大的影响它们，针对那些表现出特定个性特征的用户。

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