Achieving human-like communication with machines remains a classic, challenging topic in the field of Knowledge Representation and Reasoning and Natural Language Processing. These Large Language Models (LLMs) rely on pattern-matching rather than a true understanding of the semantic meaning of a sentence. As a result, they may generate incorrect responses. To generate an assuredly correct response, one has to "understand" the semantics of a sentence. To achieve this "understanding", logic-based (commonsense) reasoning methods such as Answer Set Programming (ASP) are arguably needed. In this paper, we describe the AutoConcierge system that leverages LLMs and ASP to develop a conversational agent that can truly "understand" human dialogs in restricted domains. AutoConcierge is focused on a specific domain-advising users about restaurants in their local area based on their preferences. AutoConcierge will interactively understand a user's utterances, identify the missing information in them, and request the user via a natural language sentence to provide it. Once AutoConcierge has determined that all the information has been received, it computes a restaurant recommendation based on the user-preferences it has acquired from the human user. AutoConcierge is based on our STAR framework developed earlier, which uses GPT-3 to convert human dialogs into predicates that capture the deep structure of the dialog's sentence. These predicates are then input into the goal-directed s(CASP) ASP system for performing commonsense reasoning. To the best of our knowledge, AutoConcierge is the first automated conversational agent that can realistically converse like a human and provide help to humans based on truly understanding human utterances.

翻译：实现与机器的类人交流仍是知识表示与推理以及自然语言处理领域中一个经典且富有挑战性的课题。当前的大型语言模型依赖模式匹配而非真正理解句子的语义含义，因此可能生成错误响应。要生成可靠正确的响应，必须"理解"句子的语义。要实现这种"理解"，基于逻辑的（常识）推理方法（如应答集编程）是必要的。本文描述的AutoConcierge系统利用LLMs和ASP开发一种对话智能体，能够真正"理解"受限领域内的人类对话。AutoConcierge专注于特定领域——根据用户偏好推荐本地餐厅。该系统能交互式理解用户话语，识别其中的缺失信息，并通过自然语言语句请求用户补充信息。当AutoConcierge确认已获取全部信息后，它将基于从人类用户处获得的偏好计算餐厅推荐方案。AutoConcierge基于我们先前开发的STAR框架，该框架使用GPT-3将人类对话转换为可捕获对话句子深层结构的谓词，这些谓词随后被输入到目标导向的s(CASP) ASP系统中进行常识推理。据我们所知，AutoConcierge是首个能像人类一样自然交流、并通过真正理解人类话语提供帮助的自动化对话智能体。