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

TL;DR

DiscASP introduces a graph-based algorithm to identify relevant, consistent concepts in answer set programs, enhancing conversational AI systems like socialbots by mimicking human-like concept retrieval.

Contribution

The paper presents a novel graph-based algorithm and system, DiscASP, for finding relevant consistent concepts in answer set programs, improving conversational AI capabilities.

Findings

DiscASP effectively finds relevant concepts in answer set programs.

Application to socialbots demonstrates improved conversational relevance.

The system mimics human-like concept retrieval in conversations.

Abstract

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.