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

DiscASP: A Graph-based ASP System for Finding Relevant Consistent Concepts with Applications to Conversational Socialbots