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Nominal Unification
 Theoretical Computer Science
, 2003
"... We present a generalisation of firstorder unification to the practically important case of equations between terms involving binding operations. A substitution of terms for variables solves such an equation if it makes the equated terms #equivalent, i.e. equal up to renaming bound names. For the a ..."
Abstract

Cited by 58 (24 self)
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We present a generalisation of firstorder unification to the practically important case of equations between terms involving binding operations. A substitution of terms for variables solves such an equation if it makes the equated terms #equivalent, i.e. equal up to renaming bound names. For the applications we have in mind, we must consider the simple, textual form of substitution in which names occurring in terms may be captured within the scope of binders upon substitution. We are able to take a `nominal' approach to binding in which bound entities are explicitly named (rather than using nameless, de Bruijnstyle representations) and yet get a version of this form of substitution that respects #equivalence and possesses good algorithmic properties. We achieve this by adapting an existing idea and introducing a key new idea. The existing idea is terms involving explicit substitutions of names for names, except that here we only use explicit permutations (bijective substitutions). The key new idea is that the unification algorithm should solve not only equational problems, but also problems about the freshness of names for terms. There is a simple generalisation of the classical firstorder unification algorithm to this setting which retains the latter's pleasant properties: unification problems involving #equivalence and freshness are decidable; and solvable problems possess most general solutions.
Macros as multistage computations: Typesafe, generative, binding macros in MacroML
 in MacroML. In the International Conference on Functional Programming (ICFP ’01
, 2001
"... ..."
Abstract
"... Most programming languages adopt static binding, but for distributed programming an exclusive reliance on static binding is too restrictive: dynamic binding is required in various guises, for example when a marshalled value is received from the network, containing identifiers that must be rebound to ..."
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Most programming languages adopt static binding, but for distributed programming an exclusive reliance on static binding is too restrictive: dynamic binding is required in various guises, for example when a marshalled value is received from the network, containing identifiers that must be rebound to local resources. Typically it is provided only by adhoc mechanisms that lack clean semantics. In this paper we adopt a foundational approach, developing core dynamic rebinding mechanisms as extensions to the simplytyped callbyvalue λcalculus. To do so we must first explore refinements of the callbyvalue reduction strategy that delay instantiation, to ensure computations make use of the most recent versions of rebound definitions. We introduce redextime and destructtime strategies. The latter forms the basis for a λmarsh calculus that supports dynamic rebinding of marshalled values, while remaining as far as possible staticallytyped. We sketch an extension of λmarsh with concurrency and communication, giving examples showing how wrappers for encapsulating untrusted code can be expressed. Finally, we show that a highlevel semantics for dynamic updating can also be based on the destructtime strategy, defining a λupdate calculus with simple primitives to