{"type":"ScholarlyArticle","title":"Search Space Expansion for Efficient Incremental Inductive Logic Programming from Streamed Data","authors":[{"name":"Mark Law","slug":"mark-law","given":"Mark","family":"Law","affiliation":"ILASP Limited , UK"},{"name":"Krysia Broda","slug":"krysia-broda","given":"Krysia","family":"Broda","affiliation":"Imperial College London , UK"},{"name":"Alessandra Russo","slug":"alessandra-russo","given":"Alessandra","family":"Russo","affiliation":"Imperial College London , UK"}],"abstract":"In the past decade, several systems for learning Answer Set Programs (ASP) have been proposed, including the recent FastLAS system. Compared to other state-of-the-art approaches to learning ASP, FastLAS is more scalable, as rather than computing the hypothesis space in full, it computes a much smaller subset relative to a given set of examples that is nonetheless guaranteed to contain an optimal solution to the task (called an OPT-sufficient subset). On the other hand, like many other Inductive Logic Programming (ILP) systems, FastLAS is designed to be run on a fixed learning task meaning that if new examples are discovered after learning, the whole process must be run again.\r\n\r\nIn many real applications, data arrives in a stream. Rerunning an ILP system from scratch each time new examples arrive is inefficient. In this paper we address this problem by presenting IncrementalLAS, a system that uses a new technique, called hypothesis space expansion, to enable a FastLAS-like OPT-sufficient subset to be expanded each time new examples are discovered. We prove that this preserves FastLAS's guarantee of finding an optimal solution to the full task (including the new examples), while removing the need to repeat previous computations. Through our evaluation, we demonstrate that running IncrementalLAS on tasks updated with sequences of new examples is significantly faster than re-running FastLAS from scratch on each updated task.","doi":"10.24963/ijcai.2022/374","url":"https://ijcai.org/proceedings/2022/374","pdf":"https://ijcai.org/proceedings/2022/0374.pdf","markdown":"https://ijcai.org/proceedings/2022/374.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/374","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Knowledge Representation and Reasoning","track":"Main Track","number":374,"pages":{"first":2697,"last":2704},"keywords":["Knowledge Representation and Reasoning: Learning and reasoning","Knowledge Representation and Reasoning: Logic Programming"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987101","length_sec":81,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985244","length_sec":710,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Agirre et al., 2016] Eneko Agirre, Aitor Gonzalez Agirre, Inigo Lopez-Gazpio, Montserrat Maritxalar, German Rigau Claramunt, and Larraitz Uria. 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