this paper we describe Elf, a meta-language intended for environments dealing with deductive systems represented in LF. While this paper is intended to include a full description of the Elf core language, we only state, but do not prove here the most important theorems regarding the basic building blocks of Elf. These proofs are left to a future paper. A preliminary account of Elf can be found in [26]. The range of applications of Elf includes theorem proving and proof transformation in various logics, definition and execution of structured operational and natural semantics for programming languages, type checking and type inference, etc. The basic idea behind Elf is to unify logic definition (in the style of LF) with logic programming (in the style of Prolog, see [22, 24]). It achieves this unification by giving types an operational interpretation, much the same way that Prolog gives certain formulas (Horn-clauses) an operational interpretation. An alternative approach to logic programming in LF has been developed independently by Pym [28]. Here are some of the salient characteristics of our unified approach to logic definition and metaprogramming. First of all, the Elf search process automatically constructs terms that can represent object-logic proofs, and thus a program need not construct them explicitly. This is in contrast to logic programming languages where executing a logic program corresponds to theorem proving in a meta-logic, but a meta-proof is never constructed or used and it is solely the programmer's responsibility to construct object-logic proofs where they are needed. Secondly, the partial correctness of many meta-programs with respect to a given logic can be expressed and proved by Elf itself (see the example in Section 5). This creates the possibilit...

ions *) and varbind = Varbind of string * term (* Variable binders , Type *) In the implementation of the term language and the type checker, we have two constants type and pi. And, yes, type is a type, though this could be avoided by introducing universes (see [16]) without any changes to the code of the unifier. As is customary, we use A ! B as an abbreviation for \Pix : A: B if x does not occur free in B. Also, however, \Pix : A: B is an abbreviation for the application pi A (x : A: B). In our formulation, then, the constant pi has type \PiA : type: ((A ! type) ! type). As an example consider a predicate constant eq of type \PiA : type: A ! A ! o (where o is the type of formulas as indicated in Section 9). The single clause eqAM M: correctly models equality, that is, a goal of the form eq AM N will succeed if M and N are unifiable. The fact that unification now has to branch can be seen by considering the goal eq int (F 1 1) 1 which has three solutions for the functional logic var...

Let E be a first-order equational theory. A translation of typed higher-order E-unification problems into a typed combinatory logic framework is presented and justified. The case in which E admits presentation as a convergent term rewriting system is treated in detail: in this situation, a modification of ordinary narrowing is shown to be a complete method for enumerating higher-order E-unifiers. In fact, we treat a more general problem, in which the types of terms contain type variables. 1 Introduction Investigation of the interaction between first-order and higher-order equational reasoning has emerged as an active line of research. The collective import of a recent series of papers, originating with [Bre88] and including (among others) [Bar90], [BG91a], [BG91b], [Dou92], [JO91] and [Oka89], is that when various typed -calculi are enriched by first-order equational theories, the validity problem is well-behaved, and furthermore that the respective computational approaches to ...