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Electronic Notes in Theoretical Computer Science
Article . 2006 . Peer-reviewed
License: CC BY NC ND
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Token-Passing Nets: Call-by-Need for Free

Authors: François-Régis Sinot;

Token-Passing Nets: Call-by-Need for Free

Abstract

AbstractRecently, encodings in interaction nets of the call-by-name and call-by-value strategies of the λ-calculus have been proposed. The purpose of these encodings was to bridge the gap between interaction nets and traditional abstract machines, which are both used to provide lower-level specifications of strategies of the λ-calculus, but in radically different ways. The strength of these encodings is their simplicity, which comes from the simple idea of introducing an explicit syntactic object to represent the flow of evaluation. In particular, no artifact to represent boxes is needed. However, these encodings purposefully follow as closely as possible the implemented strategies, call-by-name and call-by-value, hence do not benefit from the ability of interaction nets to easily represent sharing. The aim of this note is to show that sharing can indeed be achieved without adding any structure. We thus present the call-by-need strategy following the same philosophy, which is indeed not any more complicated than call-by-name. This continues the task of bridging the gap between interaction nets and abstract machines, thus pushing forward a more uniform framework for implementations of the λ-calculus.

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Keywords

call-by-value, λ-calculus, interaction net, call-by-name, Theoretical Computer Science, Computer Science(all)

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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!
4
Average
Top 10%
Average
gold