When logic programming is based on the proof theory of intuitionistic logic, it is natural to allow implications in goals and in the bodies of clauses. Attempting to prove a goal of the form D ⊃ G from the context (set of formulas) Γ leads to an attempt to prove the goal G in the extended context Γ ∪ {D}. Thus during the bottom-up search for a cut-free proof contexts, represented as the left-hand side of intuitionistic sequents, grow as stacks. While such an intuitionistic notion of context provides for elegant specifications of many computations, contexts can be made more expressive and flexible if they are based on linear logic. After presenting two equivalent formulations of a fragment of linear logic, we show that the fragment has a goal-directed interpretation, thereby partially justifying calling it a logic programming language. Logic programs based on the intuitionistic theory of hereditary Harrop formulas can be modularly embedded into this linear logic setting. Programming examples taken from theorem proving, natural language parsing, and data base programming are presented: each example requires a linear, rather than intuitionistic, notion of context to be modeled adequately. An interpreter for this logic programming language must address the problem of splitting contexts; that is, when attempting to prove a multiplicative conjunction (tensor), say G1 ⊗ G2, from the context ∆, the latter must be split into disjoint contexts ∆1 and ∆2 for which G1 follows from ∆1 and G2 follows from ∆2. Since there is an exponential number of such splits, it is important to delay the choice of a split as much as possible. A mechanism for the lazy splitting of contexts is presented based on viewing proof search as a process that takes a context, consumes part of it, and returns the rest (to be consumed elsewhere). In addition, we use collections of Kripke interpretations indexed by a commutative monoid to provide models for this logic programming language and show that logic programs admit a canonical model. 1

The variety of semantical approaches that have been invented for logic programs is quite broad, drawing on classical and many-valued logic, lattice theory, game theory, and topology. One source of this richness is the inherent non-monotonicity of its negation, something that does not have close parallels with the machinery of other programming paradigms. Nonetheless, much of the work on logic programming semantics seems to exist side by side with similar work done for imperative and functional programming, with relatively minimal contact between communities. In this paper we summarize one variety of approaches to the semantics of logic programs: that based on fixpoint theory. We do not attempt to cover much beyond this single area, which is already remarkably fruitful. We hope readers will see parallels with, and the divergences from the better known fixpoint treatments developed for other programming methodologies.

Abstract not found

Abstract: A universally quantified goal can be interpreted intensionally, that is, the goal ∀x.G(x) succeeds if for some new constant c, the goal G(c) succeeds. The constant c is, in a sense, given a scope: it is introduced to solve this goal and is “discharged ” after the goal succeeds or fails. This interpretation is similar to the interpretation of implicational goals: the goal D ⊃ G should succeed if when D is assumed, the goal G succeeds. The assumption D is discharged after G succeeds or fails. An interpreter for a logic programming language containing both universal quantifiers and implications in goals and the body of clauses is described. In its non-deterministic form, this interpreter is sound and complete for intuitionistic logic. Universal quantification can provide lexical scoping of individual, function, and predicate constants. Several examples are presented to show how such scoping can be used to provide a Prolog-like language with facilities data types, and encapsulation of state.

We present a logic programming language that uses implications and universal quantifiers in goals and the bodies of clauses to provide a simple scoping mechanism for program clauses and constants. Within this language it is possible to define a simple notion of parametric module and local constant. Given this ability to structure programs, we explore how object-oriented programming, where objects are viewed as abstractions with behaviors, state, and inheritance, might be accommodated. To capture the notion of mutable state, we depart from the pure logic setting by adding a declaration that certain local predicates are deterministic (they succeed at most once). This declaration, along with a goal-continuation passing style of programming is adequate to model the state of objects. We also examine a few aspects of how having objects embedded in logic programming can be used to enrich the notion of object: for examples, objects may be partial (that is, may contain free variables) and non-d...

Definite Clause Grammars (DCGs) have proved valuable to computational linguists since they can be used to specify phrase structured grammars. It is well known how to encode DCGs in Horn clauses. Some linguistic phenomena, such as filler-gap dependencies, are difficult to account for in a completely satisfactory way using simple phrase structured grammar. In the literature of logic grammars there have been several attempts to tackle this problem by making use of special arguments added to the DCG predicates corresponding to the grammatical symbols. In this paper we take a different line, in that we account for filler-gap dependencies by encoding DCGs within hereditary Harrop formulas, an extension of Horn clauses (proposed elsewhere as a foundation for logic programming) where implicational goals and universally quantified goals are permitted. Under this approach, filler-gap dependencies can be accounted for in terms of the operational semantics underlying hereditary Harrop formulas, in a way reminiscent of the treatment of such phenomena in Generalized Phrase Structure Grammar (GPSG). The main features involved in this new formulation of DCGs are mechanisms for providing scope to constants and program clauses along with a mild use of λ-terms and λ-conversion. 1

One of the distinguishing features of logic programming seems to be the notion of goal-directed provability, i.e. that the structure of the goal is used to determine the next step in the proof search process. It is known that by restricting the class of formulae it is possible to guarantee that a certain class of proofs, known as uniform proofs, are complete with respect to provability in intuitionistic logic. In this paper we explore the relationship between uniform proofs and classes of formulae more deeply. Firstly we show that uniform proofs arise naturally as a normal form for proofs in first-order intuitionistic sequent calculus. Next we show that the class of formulae known as hereditary Harrop formulae are intimately related to uniform proofs, and that we may extract such formulae from uniform proofs in two different ways. We also give results which may be interpreted as showing that hereditary Harrop formulae are the largest class of formulae for which uniform proo...

We introduce the foundational issues involved in incorporating the Negation as Failure (NAF) rule into the framework of first-order hereditary Harrop formulae of Miller et al. This is a larger class of formulae than Horn clauses, and so the technicalities are more intricate than in the Horn clause case. As programs may grow during execution in this framework, the role of NAF and the Closed World Assumption (CWA) need some modification, and for this reason we introduce the notion of a completely defined predicate, which may be thought of as a localisation of the CWA. We also show how this notion may be used to define a notion of NAF for a more general class of goals than literals alone. We also show how an extensional notion of universal quantification may be incorporated. This makes our framework somewhat different from that of Miller et al., but not essentially so. We also show how to construct a Kripke-like model for the extended class of programs. This is essentially a de...

Proof procedures are an essential part of logic applied to artificial intelligence tasks, and form the basis for logic programming languages. As such, many of the chapters throughout this handbook utilize, or study, proof procedures. The study of proof procedures that are useful in artificial intelligence would require a large book so we focus on proof procedures that relate to logic programming. We begin with the resolution procedures that influenced the definition of SLD-resolution, the procedure upon which Prolog is built. Starting with the general resolution procedure we move through linear resolution to a very restricted linear resolution, SLresolution, which actually is not a resolution restriction, but a variant using an augmented logical form. (SL-resolution actually is a derivative of the Model Elimination procedure, which was developed independently of resolution.) We then consider logic programming itself, reviewing SLD-resolution and then describing a general criterion for ...

Higher-order hereditary Harrop formulas, the underlying logical foundation of λProlog [NM88], are more expressive than first-order Horn clauses, the logical foundation of Prolog. In particular, various forms of scoping and abstraction are supported by the logic of higherorder hereditary Harrop formulas while they are not supported by firstorder Horn clauses. Various papers have argued that the scoping and abstraction available in this richer logic can be used to provide for modular programming [Mil89b], abstract data types [Mil89a], and state encapsulation [HM90]. None of these papers, however, have dealt with the problems of programming-in-the-large, that is, the essentially linguistic problems of putting together various different textual sources of code found, say, in different files on a persistent store into one logic program. In this paper, I propose a module system for λProlog and shall focus mostly on its static semantics.