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https://doi.org/10.1...arrow_drop_down
https://doi.org/10.1007/bfb001...
Part of book or chapter of book . 2005 . Peer-reviewed
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DBLP
Conference object . 2017
Data sources: DBLP
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Higher order conditional rewriting and narrowing

Authors: Jürgen Avenhaus; Carlos Loría-Sáenz;

Higher order conditional rewriting and narrowing

Abstract

First order conditional rewrite systems R have been extensively studied. If R is confluent and terminating, then narrowing is a sound and complete procedure to compute all solutions of a goal s = t modulo R. Recently there has been developed a satisfactory way to combine higher order terms and unconditional rewriting. In this paper we first show that this approach can be carried over to conditional higher order rewrite systems. Then we study narrowing using higher order rewrite systems. A naive translation of first order narrowing may lead to unsolvable unification problems. So we restrict to ”quasi first order” goals and ”simple” rewrite systems.

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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Average
Average
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