Answering why-questions using probabilistic logic programming

Abdus Salam*, Rolf Schwitter, Mehmet A. Orgun

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review


We present a novel architecture of a closed domain question answering system that learns to answer why-questions from a small number of example interpretations. We use a probabilistic logic programming framework that can learn probabilities for rules from positive and negative example interpretations. These rules are then used by a meta-interpreter to generate an explanation in the form of a proof for a why-question. The explanation is displayed as an answer to the question together with a probability. In certain contexts, follow-up questions can be asked that conditionally depend on these why-questions and have an effect on the probability of the subsequent answer. The presented approach is a contribution to explainable artificial intelligence that aims to take machine learning out of the black-box.

Original languageEnglish
Title of host publicationAI 2019
Subtitle of host publicationAdvances in Artificial Intelligence - 32nd Australasian Joint Conference, 2019, Proceedings
EditorsJixue Liu, James Bailey
Place of PublicationSwitzerland
Number of pages12
ISBN (Electronic)9783030352882
ISBN (Print)9783030352875
Publication statusPublished - 2019
Event32nd Australasian Joint Conference on Artificial Intelligence, AI 2019 - Adelaide, Australia
Duration: 2 Dec 20195 Dec 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11919 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference32nd Australasian Joint Conference on Artificial Intelligence, AI 2019


  • Meta-interpreter
  • Natural language processing
  • Probabilistic logic programming
  • why-questions


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