{"type":"ScholarlyArticle","title":"Induction of Interpretable Possibilistic Logic Theories from Relational Data","authors":[{"name":"Ondrej Kuzelka","slug":"ondrej-kuzelka","given":"Ondrej","family":"Kuzelka","affiliation":"Cardiff University , UK"},{"name":"Jesse Davis","slug":"jesse-davis","given":"Jesse","family":"Davis","affiliation":"KU Leuven , Belgium"},{"name":"Steven Schockaert","slug":"steven-schockaert","given":"Steven","family":"Schockaert","affiliation":"Cardiff University , UK"}],"abstract":"The field of statistical relational learning (SRL) is concerned with learning probabilistic models from relational data. Learned SRL models are typically represented using some kind of weighted logical formulas, which makes them considerably more interpretable than those obtained by e.g. neural networks. In practice, however, these models are often still difficult to interpret correctly, as they can contain many formulas that interact in non-trivial ways and weights do not always have an intuitive meaning. To address this, we propose a new SRL method which uses possibilistic logic to encode relational models. Learned models are then essentially stratified classical theories, which explicitly encode what can be derived with a given level of certainty. Compared to Markov Logic Networks (MLNs), our method is faster and produces considerably more interpretable models.","doi":"10.24963/ijcai.2017/160","url":"https://ijcai.org/proceedings/2017/160","pdf":"https://ijcai.org/proceedings/2017/0160.pdf","markdown":"https://ijcai.org/proceedings/2017/160.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/160","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Knowledge Representation, Reasoning, and Logic","track":"Main track","number":160,"pages":{"first":1153,"last":1159},"keywords":["Machine Learning: Relational Learning","Knowledge Representation, Reasoning, and Logic: Common-Sense Reasoning","Uncertainty in AI: Uncertainty in AI","Uncertainty in AI: Uncertainty Representations"],"videos":[],"references":[{"position":1,"raw":"[Alphonse and Osmani, 2008] Erick Alphonse and Aomar Osmani. 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