Grice's Cooperative Principle (1975) describes the implicit maxims that guide conversation between humans. As humans begin to interact with non-human dialogue systems more frequently and in a broader scope, an important question emerges: what principles govern those interactions? The present study addresses this question by evaluating human-AI interactions using Grice's four maxims; we demonstrate that humans do, indeed, apply these maxims to interactions with AI, even making explicit references to the AI's performance through a Gricean lens. Twenty-three participants interacted with an American English-speaking Alexa and rated and discussed their experience with an in-lab researcher. Researchers then reviewed each exchange, identifying those that might relate to Grice's maxims: Quantity, Quality, Manner, and Relevance. Many instances of explicit user frustration stemmed from violations of Grice's maxims. Quantity violations were noted for too little but not too much information, while Quality violations were rare, indicating trust in Alexa's responses. Manner violations focused on speed and humanness. Relevance violations were the most frequent, and they appear to be the most frustrating. While the maxims help describe many of the issues participants encountered, other issues do not fit neatly into Grice's framework. Participants were particularly averse to Alexa initiating exchanges or making unsolicited suggestions. To address this gap, we propose the addition of human Priority to describe human-AI interaction. Humans and AIs are not conversational equals, and human initiative takes priority. We suggest that the application of Grice's Cooperative Principles to human-AI interactions is beneficial both from an AI development perspective and as a tool for describing an emerging form of interaction.

中文翻译：

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通过 Gricean 镜头的人机交互

Grice 的合作原则 (1975) 描述了指导人与人之间对话的隐含准则。随着人类开始在更广泛的范围内更频繁地与非人类对话系统进行交互，一个重要的问题出现了：哪些原则管理着这些交互？本研究通过使用格莱斯的四个准则评估人与人工智能的交互来解决这个问题；我们证明，人类确实将这些准则应用于与 AI 的交互，甚至通过 Gracean 镜头明确提及 AI 的性能。23 名参与者与讲美国英语的 Alexa 互动，并与实验室研究人员一起评价和讨论他们的经历。研究人员随后审查了每个交流，确定了可能与格莱斯格言相关的那些：数量、质量、方式和相关性。许多明确的用户沮丧事例源于违反格莱斯的格言。数量违规是因为信息太少但不是太多，而质量违规很少见，表明对 Alexa 的响应的信任。违规行为侧重于速度和人性化。相关性违规是最常见的，而且似乎也是最令人沮丧的。虽然格言帮助描述了参与者遇到的许多问题，但其他问题并不完全适合 Grice 的框架。参与者特别反对 Alexa 发起交流或主动提出建议。为了解决这个差距，我们建议添加人类优先级来描述人机交互。人类和人工智能在对话上不是平等的，人类的主动性优先。我们建议应用 Grice'