Static learning for an adaptative theorem prover View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

1991

AUTHORS

C. Belleannee , J. Nicolas

ABSTRACT

An adaptative theorem prover is a system able to modify its current set of inference rules in order to improve its performance on a specific domain. We address here the issue of the generation of inference rules, without considering the selection and deletion issues. We especially develop the treatment of repeating events within a proof. We specify a general representation for objects to be learned in this framework, that is macro-connectives and macro-inference-rules and show how they may be generated from the primitive set of inference rules. Our main contribution consists to show that a form of analytical, static learning, is possible in this domain. More... »

PAGES

298-311

Book

TITLE

Machine Learning — EWSL-91

ISBN

978-3-540-53816-5
978-3-540-46308-5

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bfb0017022

DOI

http://dx.doi.org/10.1007/bfb0017022

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1012199847


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