FLOC 2018: FEDERATED LOGIC CONFERENCE 2018
Learning Commonsense Knowledge through Interactive Dialogue

Authors: Benjamin Wu, Alessandra Russo, Mark Law and Katsumi Inoue

Paper Information

Title:Learning Commonsense Knowledge through Interactive Dialogue
Authors:Benjamin Wu, Alessandra Russo, Mark Law and Katsumi Inoue
Proceedings:ICLP Proceedings of ICLP 2018
Editors: Paul Tarau and Alessandro Dal Palu'
Keywords:Commonsense Reasoning, Answer Set Programming, Event Calculus, Inductive Logic Programming
Abstract:

ABSTRACT. One of the most difficult problems in Artificial Intelligence is related to acquiring commonsense knowledge -- to create a collection of facts and information that an ordinary person should know. In this work, we present a system that, from a limited background knowledge, is able to learn to form simple concepts through interactive dialogue with a user. We approach the problem using a syntactic parser, along with a mechanism to check for synonymy, to translate sentences into a logical formulas represented in Event Calculus using Answer Set Programming (ASP). Reasoning and learning tasks are then automatically generated for the translated text, with learning being initiated through question and answering. The system is capable of learning with no contextual knowledge prior to the dialogue. The system has been evaluated on stories inspired by the Facebook's bAbI's question-answering tasks, and through appropriate question and answering is able to respond accurately to these dialogues.

Pages:20
Talk:Jul 17 17:00 (Session 123: Technical Communications II)
Paper: