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.
翻译：我们考虑了在谈话性自主系统中找到相关一致概念的问题,特别是实现对话性社交机器人的问题。关于不同主题的常识可以作为一个解答组合程序来代表。然而,为了推进对话,我们需要解决找到相关一致概念的问题,即,在目前讨论的话题的“邻里”中找到可用于推进对话的一致知识。传统的ASP解答器将产生整个解答集,该解答集将被剥夺各个原子(概念)之间的所有关联,因此无法用于找到相关的一致概念。同样,以目标为主的ASP实施将只找到与查询直接相关的概念。我们介绍DiscASP系统,它将找到与某个特定议题相关的部分一致的模式,其方式与人类如何找到该主题类似。DiscASP基于一种基于图表的新式算法,以寻找一个解答性组合程序的稳定模式。我们介绍了DiscASP的算法、其实施及其应用于开发一个对话性社会博特。
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