We consider the problem of finding relevant consistent concepts in a conversational AI system, particularly, for realizing a conversational socialbot. Commonsense knowledge about various topics can be represented as an answer set program. However, to advance the conversation, we need to solve the problem of finding relevant consistent concepts, i.e., find consistent knowledge in the "neighborhood" of the current topic being discussed that can be used to advance the conversation. Traditional ASP solvers will generate the whole answer set which is stripped of all the associations between the various atoms (concepts) and thus cannot be used to find relevant consistent concepts. Similarly, goal-directed implementations of ASP will only find concepts directly relevant to a query. We present the DiscASP system that will find the partial consistent model that is relevant to a given topic in a manner similar to how a human will find it. DiscASP is based on a novel graph-based algorithm for finding stable models of an answer set program. We present the DiscASP algorithm, its implementation, and its application to developing a conversational socialbot.

中文翻译：

---

DiscASP：一种基于图形的 ASP 系统，用于通过对话式社交机器人的应用程序查找相关的一致概念

我们考虑在对话式 AI 系统中寻找相关一致概念的问题，特别是为了实现对话式社交机器人。关于各种主题的常识知识可以表示为答案集程序。然而，为了推进对话，我们需要解决寻找相关一致概念的问题，即在当前正在讨论的话题的“邻域”中找到可用于推进对话的一致知识。传统的 ASP 求解器将生成整个答案集，该答案集去除了各种原子（概念）之间的所有关联，因此无法用于找到相关的一致概念。类似地，ASP 的目标导向实现只会找到与查询直接相关的概念。我们展示了 DiscASP 系统，它将以类似于人类如何找到它的方式找到与给定主题相关的部分一致模型。DiscASP 基于一种新颖的基于图的算法，用于寻找答案集程序的稳定模型。我们介绍了 DiscASP 算法、它的实现以及它在开发会话社交机器人中的应用。