{"type":"ScholarlyArticle","title":"Learning Efficient Logic Programs","authors":[{"name":"Andrew Cropper","slug":"andrew-cropper","given":"Andrew","family":"Cropper","affiliation":""}],"abstract":"Most logic-based machine learning algorithms rely on an Occamist bias where textual simplicity of hypotheses is optimised. This approach, however, fails to distinguish between the efficiencies of hypothesised programs, such as quick sort (O(n log n)) and bubble sort (O(n^2)). We address this issue by considering techniques to minimise both the resource complexity and textual complexity of hypothesised programs. We describe an algorithm proven to learn optimal resource complexity robot strategies, and we propose future work to generalise this approach to a broader class of logic programs.","doi":null,"url":"https://ijcai.org/proceedings/2015/627","pdf":"https://ijcai.org/proceedings/2015/0627.pdf","markdown":"https://ijcai.org/proceedings/2015/627.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/627","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":627,"pages":{"first":4359,"last":4359},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}