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CASPR: A Commonsense Reasoning-based Conversational Socialbot

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arxiv 2110.05387 v1 pith:XV53BJAA submitted 2021-10-11 cs.AI cs.HCcs.LO

Kinjal Basu , Huaduo Wang , Nancy Dominguez , Xiangci Li , Fang Li , Sarat Chandra Varanasi , Gopal Gupta This is my paper · ORCID
classification cs.AIcs.HCcs.LO
keywords casprsocialbotcommonsenseableconversationconversationaldesignknowledge
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We report on the design and development of the CASPR system, a socialbot designed to compete in the Amazon Alexa Socialbot Challenge 4. CASPR's distinguishing characteristic is that it will use automated commonsense reasoning to truly "understand" dialogs, allowing it to converse like a human. Three main requirements of a socialbot are that it should be able to "understand" users' utterances, possess a strategy for holding a conversation, and be able to learn new knowledge. We developed techniques such as conversational knowledge template (CKT) to approximate commonsense reasoning needed to hold a conversation on specific topics. We present the philosophy behind CASPR's design as well as details of its implementation. We also report on CASPR's performance as well as discuss lessons learned.

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  1. Reliable Conversational Agents under ASP Control that Understand Natural Language

    cs.LO 2025-02 reject novelty 4.0 of 10

    A neuro-symbolic conversational framework uses LLMs purely as semantic parsers and ASP for reasoning, with only preliminary evidence supporting the claimed reliability.

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