Lifting Term Rewriting Derivations in Constructor Systems by Using Generators
January 09, 2015 Β· Declared Dead Β· π PROLE
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
AdriΓ‘n Riesco, Juan RodrΓguez-HortalΓ‘
arXiv ID
1501.02035
Category
cs.PL: Programming Languages
Citations
0
Venue
PROLE
Last Checked
4 months ago
Abstract
Narrowing is a procedure that was first studied in the context of equational E-unification and that has been used in a wide range of applications. The classic completeness result due to Hullot states that any term rewriting derivation starting from an instance of an expression can be "lifted" to a narrowing derivation, whenever the substitution employed is normalized. In this paper we adapt the generator- based extra-variables-elimination transformation used in functional-logic programming to overcome that limitation, so we are able to lift term rewriting derivations starting from arbitrary instances of expressions. The proposed technique is limited to left-linear constructor systems and to derivations reaching a ground expression. We also present a Maude-based implementation of the technique, using natural rewriting for the on-demand evaluation strategy.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Programming Languages
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
R.I.P.
π»
Ghosted
Glow: Graph Lowering Compiler Techniques for Neural Networks
R.I.P.
π»
Ghosted
Learnable Programming: Blocks and Beyond
R.I.P.
π»
Ghosted
Scenic: A Language for Scenario Specification and Scene Generation
R.I.P.
π»
Ghosted
Vandal: A Scalable Security Analysis Framework for Smart Contracts
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted