Learning Possibilistic Logic Theories from Default Rules

Kuzelka, Ondrej; Davis, Jesse; Schockaert, Steven

Learning Possibilistic Logic Theories from Default Rules

Ondrej Kuzelka, Jesse Davis, Steven Schockaert

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Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 1167-1172.

Abstract

We introduce a setting for learning possibilistic logic theories from defaults of the form if alpha then typically beta. We first analyse this problem from the point of view of machine learning theory, determining the VC dimension of possibilistic stratifications as well as the complexity of the associated learning problems, after which we present a heuristic learning algorithm that can easily scale to thousands of defaults. An important property of our approach is that it is inherently able to handle noisy and conflicting sets of defaults. Among others, this allows us to learn possibilistic logic theories from crowdsourced data and to approximate propositional Markov logic networks using heuristic MAP solvers. We present experimental results that demonstrate the effectiveness of this approach.

Ondrej Kuzelka, Jesse Davis, and Steven Schockaert. 2016. Learning Possibilistic Logic Theories from Default Rules. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16. IJCAI, 1167–1172. https://ijcai.org/proceedings/2016/169

@inproceedings{ijcai2016p169,
  title     = {Learning Possibilistic Logic Theories from Default Rules},
  author    = {Kuzelka, Ondrej and Davis, Jesse and Schockaert, Steven},
  booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
               Artificial Intelligence, {IJCAI-16}},
  publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
  editor    = {Subbarao Kambhampati},
  pages     = {1167--1172},
  year      = {2016},
  month     = {7},
  url       = {https://ijcai.org/proceedings/2016/169},
}

TY  - CPAPER
TI  - Learning Possibilistic Logic Theories from Default Rules
AU  - Kuzelka, Ondrej
AU  - Davis, Jesse
AU  - Schockaert, Steven
T2  - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16
PB  - AAAI Press / International Joint Conferences on Artificial Intelligence
PY  - 2016
SP  - 1167
EP  - 1172
UR  - https://ijcai.org/proceedings/2016/169
ER  -