Abstract
Why-questions try to get to the bottom of the matter and ask for explanations. In this paper, we show how we can learn the complete structure of probabilistic rules for a question-answering system from example interpretations. These rules are then used by a meta-interpreter to find answers in the form of explanations for why-questions for a particular application domain.
| Original language | English |
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| Number of pages | 9 |
| Publication status | Published - 2019 |
| Event | 1st International Workshop on Interpretability: Methodologies and Algorithms (IMA 2019) part of AI 2019 and AusDM 2019 - Adelaide, Australia Duration: 2 Dec 2019 → 2 Dec 2019 |
Conference
| Conference | 1st International Workshop on Interpretability: Methodologies and Algorithms (IMA 2019) part of AI 2019 and AusDM 2019 |
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| Country/Territory | Australia |
| City | Adelaide |
| Period | 2/12/19 → 2/12/19 |
Keywords
- why-questions
- explainability
- probabilistic logic programming
- probabilistic rule learning