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：实现与机器的人类化交流仍是知识表示与推理及自然语言处理领域中的一个经典且具有挑战性的课题。大型语言模型依赖模式匹配，而非对句子语义含义的真正理解，因此可能生成不正确的响应。要生成具有确定性的正确响应，必须“理解”句子的语义。为实现这一“理解”，基于逻辑的（常识）推理方法（如答案集编程）被认为不可或缺。本文描述了AutoConcierge系统，该系统利用大型语言模型和ASP开发了一个能够在受限领域中真正“理解”人类对话的对话智能体。AutoConcierge专注于特定领域——根据用户偏好为其推荐本地餐厅。该智能体将以交互方式理解用户话语，识别其中缺失的信息，并通过自然语言句子请求用户提供该信息。一旦AutoConcierge确定已获取全部信息，它将根据从人类用户处获得的用户偏好计算餐厅推荐。AutoConcierge基于我们此前开发的STAR框架，该框架使用GPT-3将人类对话转化为谓词，以捕获对话句子的深层结构。这些谓词随后输入到面向目标的s(CASP) ASP系统中，用于执行常识推理。据我们所知，AutoConcierge是首个能够像人类一样进行逼真对话，并基于对人类话语的真正理解为人类提供帮助的自动化对话智能体。