Conversational systems have become an element of everyday life for billions of users who use speech‐based interfaces to services, engage with personal digital assistants on smartphones, social media chatbots, or smart speakers. One of the most complex tasks in the development of these systems is to design the dialogue model, the logic that provided a user input selects the next answer. The dialogue model must also consider mechanisms to adapt the response of the system and the interaction style according to different groups and user profiles. Rule‐based systems are difficult to adapt to phenomena that were not taken into consideration at design‐time. However, many of the systems that are commercially available are based on rules, and so are the most widespread tools for the development of chatbots and speech interfaces. In this article, we present a proposal to: (a) automatically generate the dialogue rules from a dialogue corpus through the use of evolving algorithms, (b) adapt the rules according to the detected user intention. We have evaluated our proposal with several conversational systems of different application domains, from which our approach provided an efficient way for adapting a set of dialogue rules considering user utterance clusters.

中文翻译：

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使用意图聚类和模糊规则的自适应对话管理

会话系统已经成为数十亿用户的日常生活中的一部分，他们使用基于语音的界面进行服务，与智能手机，社交媒体聊天机器人或智能扬声器上的个人数字助理互动。这些系统开发中最复杂的任务之一是设计对话模型，提供用户输入的逻辑选择下一个答案。对话模型还必须考虑根据不同的组和用户配置文件来适应系统响应和交互样式的机制。基于规则的系统很难适应设计时未考虑的现象。但是，许多可商购的系统都是基于规则的，因此，聊天机器人和语音接口开发的最广泛的工具也是基于规则的。在这篇文章中，我们提出一项建议：（a）通过使用不断发展的算法从对话语料库自动生成对话规则，（b）根据检测到的用户意图调整规则。我们已经使用不同应用程序域的几个对话系统评估了我们的建议，从中我们的方法提供了一种有效的方法，可以在考虑用户话语集群的情况下调整一组对话规则。