Semantic Model for Induction of First Order Theories
Proceedings of the Twelfth International Joint Conference on Artificial Intelligence. Learning Be Knowledge Acquisition. Pages 685-690.
Abstract
This paper presents an extension of the inverse resolution framework, which is a logical model of theory induction in first order logic. We first propose a definition of completeness for inductive procedures and exhibit some limitations of inverse resolution with respect to this definition. We then propose our model of inversion of logical entailment that extends inverse resolution with regard to these limitations. Two operators are formalized that implement this formal framework and we prove that they are complete. In the last part of the paper, we propose two possible controls for these operators, to restrict the search for an acceptable theory. These two controls express some semantic properties of the theory that the system is searching for.
@inproceedings{ijcai1991p100,
title = {Semantic Model for Induction of First Order Theories},
author = {Rouveirol, Céline},
booktitle = {Proceedings of the Twelfth International Joint Conference on
Artificial Intelligence, {IJCAI-91}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {John Mylopoulos and Ray Reiter},
pages = {685--690},
year = {1991},
month = {8},
note = {Learning Be Knowledge Acquisition},
url = {https://ijcai.org/proceedings/1991/100},
}
TY - CPAPER TI - Semantic Model for Induction of First Order Theories AU - Rouveirol, Céline T2 - Proceedings of the Twelfth International Joint Conference on Artificial Intelligence, IJCAI-91 PB - International Joint Conferences on Artificial Intelligence PY - 1991 SP - 685 EP - 690 UR - https://ijcai.org/proceedings/1991/100 ER -Copied
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