Verification-based Learning: A Generalized Strategy for Inferring Problem-Reduction Methods
Proceedings of the Ninth International Joint Conference on Artificial Intelligence. Learning and Acquisition. Pages 616-622.
Abstract
A major impediment to the develo pment of high-performance knowledge-based systems arises from the prohibitive effort involved in equipping these systems with a sufficient set of problem-solving methods . Thus , one imp o rtant research problem in Machine Learning has been the st udy of techniques for inferring problem-solving methods from e xampl es. Although a number of techniques for learning problem-s olving methods have been described in th e literature , all of them assume a .tate-apace model of problem-solving. In this paper we describe a new te chnique for learning problem-reduction meth ods, Verification-Based Learning (VBL) , which extends the earlier techniques to the problem-reduction formulation of pr oblem- solving . We illustrate the VBL technique with examples drawn from circuit design and symbolic integration.
@inproceedings{ijcai1985p119,
title = {Verification-based Learning: A Generalized Strategy for Inferring Problem-Reduction Methods},
author = {Mahadevan, Sridhar},
booktitle = {Proceedings of the Ninth International Joint Conference on
Artificial Intelligence, {IJCAI-85}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Aravind Joshi},
pages = {616--622},
year = {1985},
note = {Learning and Acquisition},
url = {https://ijcai.org/proceedings/1985/119},
}
TY - CPAPER TI - Verification-based Learning: A Generalized Strategy for Inferring Problem-Reduction Methods AU - Mahadevan, Sridhar T2 - Proceedings of the Ninth International Joint Conference on Artificial Intelligence, IJCAI-85 PB - International Joint Conferences on Artificial Intelligence PY - 1985 SP - 616 EP - 622 UR - https://ijcai.org/proceedings/1985/119 ER -Copied
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