Answering Why-Questions Using Probabilistic Logic Programming

• Published in: AI 2019: Advances in Artificial Intelligence Vol. 11919; pp. 153 - 164
• Main Authors: Salam, Abdus, Schwitter, Rolf, Orgun, Mehmet A.
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2019; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Meta-interpreter; Natural language processing; Probabilistic logic programming; questions
• ISBN: 3030352870; 9783030352875
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-35288-2_13

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.