Learning Efficient Logic Programs

Cropper, Andrew

Learning Efficient Logic Programs

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Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Doctoral Consortium. Pages 4359-4359.

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.

Andrew Cropper. 2015. Learning Efficient Logic Programs. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15. IJCAI, 4359–4359. https://ijcai.org/proceedings/2015/627

@inproceedings{ijcai2015p627,
  title     = {Learning Efficient Logic Programs},
  author    = {Cropper, Andrew},
  booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-15}},
  publisher = {International Joint Conferences on Artificial Intelligence},
  editor    = {Qiang Yang and Michael Wooldridge},
  pages     = {4359--4359},
  year      = {2015},
  month     = {7},
  note      = {Doctoral Consortium},
  url       = {https://ijcai.org/proceedings/2015/627},
}

TY  - CPAPER
TI  - Learning Efficient Logic Programs
AU  - Cropper, Andrew
T2  - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15
PB  - International Joint Conferences on Artificial Intelligence
PY  - 2015
SP  - 4359
EP  - 4359
UR  - https://ijcai.org/proceedings/2015/627
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