Imperial College London
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ProfessorAlessandraRusso

Faculty of Engineering, Department of Computing

Professor in Applied Computational Logic
 
 
 
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Contact

 

+44 (0)20 7594 8312a.russo Website

 
 
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Location

 

560Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Law:2021,
author = {Law, M and Russo, A and Broda, K and Bertino, E},
pages = {1936--1943},
publisher = {IJCAI},
title = {Scalable non-observational predicate learning in ASP},
url = {https://www.ijcai.org/proceedings/2021/267},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - 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.
AU  - Law,M
AU  - Russo,A
AU  - Broda,K
AU  - Bertino,E
EP  - 1943
PB  - IJCAI
PY  - 2021///
SN  - 1045-0823
SP  - 1936
TI  - Scalable non-observational predicate learning in ASP
UR  - https://www.ijcai.org/proceedings/2021/267
UR  - http://hdl.handle.net/10044/1/90623
ER  -
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