Learning Commonsense Knowledge through Interactive Dialogue

EasyChair Preprint 224

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.

Keyphrases: Answer Set Programming, Event Calculus, Inductive Logic Programming, commonsense reasoning

@booklet{EasyChair:224,
  author    = {Benjamin Wu and Alessandra Russo and Mark Law and Katsumi Inoue},
  title     = {Learning Commonsense Knowledge through Interactive Dialogue},
  doi       = {10.29007/lrph},
  howpublished = {EasyChair Preprint 224},
  year      = {EasyChair, 2018}}