Scalable non-observational predicate learning in ASP
File(s)
Accepted version
Author(s)
Law, Mark
Russo, Alessandra
Broda, Krysia
Bertino, Elisa
Type
Conference Paper
Abstract
Recently, novel ILP systems under the answer set semantics have been proposed, some of which are robust to noise and scalable over large hypothesis spaces. One such system is FastLAS, which is significantly faster than other state-of-the-art ASP-based ILP systems. FastLAS is, however, only capable of Observational Predicate Learning (OPL),where the learned hypothesis defines predicates that are directly observed in the examples. It cannot learn knowledge that is indirectly observable, such as learning causes of observed events. This class of problems, known as non-OPL, is known to be difficult to handle in the context of non-monotonic semantics. Solving non-OPL learning tasks whilst preserving scalability is a challenging open problem. We address this problem with a new abductive method for translating examples of a non-OPL task to a set of examples, called possibilities, such that the original example is covered if at least one of the possibilities is covered. This new method al-lows an ILP system capable of performing OPL tasks to be “upgraded” to solve non-OPL tasks. In particular, we present our new FastNonOPL system, which upgrades FastLAS with the new possibility generation. We compare it to other state-of-the-art ASP-based ILP systems capable of solving non-OPL tasks, showing that FastNonOPL is significantly faster, and in many cases more accurate, than these other systems.
Date Issued
2021-08-19
Date Acceptance
2021-04-29
Citation
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021, pp.1936-1943
ISSN
1045-0823
Publisher
IJCAI
Start Page
1936
End Page
1943
Journal / Book Title
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
Copyright Statement
Copyright reserved
Sponsor
IBM United Kingdom Ltd
Identifier
https://www.ijcai.org/proceedings/2021/267
Grant Number
PO 4603 458 249
Source
IJCAI
Publication Status
Published
Start Date
2021-08-19
Finish Date
2021-08-26
Coverage Spatial
Montreal, Canada (Virtual)
Date Publish Online
2021-08-19