  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.
