Gianfranco Rossi

Extending Horn clause logic with implication goals

Abstract The paper deals with the problem of extending positive Horn clause logic by introducing implication in goals as a tool for programs structuring. We allow a goal G i in a clause G 1 ∧⋯∧ G n → A to be not only an atom but also an implication D ⊃ G (we shall it an implication goal ), where D is a set of clauses and G a goal. This extension of the language allows local definitions of clauses in logic programs. In fact, an implication goal D ⊃ G can be thought of as a block ( D , G ), where D is the set of local clause declarations. In this paper we define a language with blocks in which, as in conventional block structured programming languages, static scope rules have been chosen for locally defined clauses. We analyse static scope rules, where a goal can refer only to clauses defined in statically surrounding blocks, and we compare this extension with other proposals in the literature. We argue, on account of both implementative and semantic considerations, that this kind of block structured language is a very natural extension of Horn clauses when used as a programming language. We show it by defining an operational, fixpoint and model–theoretic semantics which are extensions of the standard ones, and by proving their equivalence. We show that static scope rules can be obtained by interpreting → as classical and ⇒ as intuitionistic implication with respect to Herbrand interpretations.

Lazy Unification Algorithms for Canonical Rewrite Systems

Publisher Summary This chapter describes a general approach to semantic unification based on the repeated application of a few simple transformations to sets of equations or multi equations. Starting from the most general nondeterministic algorithm that applies transformations in any order, particular algorithms can be derived. A more substantial improvement is to insert in the algorithm a normalisation step, retaining anyway the outermost style of the algorithm, as many nondeterministic choices are removed in this way. The chapter discusses that it is also easy to modify the algorithms so as to avoid rewriting the subterms in the left-hand parts of rewrite rules, thus, achieving behavior similar to narrowing. This is done by marking all functors that one does not want to rewrite, that is, the functors occurring in the left-hand sides of the rules. Thus, each functor f has two versions, f and f *, and terms with functor f * cannot be rewritten. Instead f and f * are regarded as the same symbol by term decomposition. The chapter presents an investigation of the practical possibility of using the semantic unification algorithms as interpreters of a logic and functional language, by extending the well-known techniques for implementing logic languages to this more general case.

Users of prolog in implementation of expert systems

Prolog is becoming a popular language in A. I. applications and particularly in the implementation of knowledge based expert systems. We have identified three different uses of Prolog: (1) building expert systems directly in ordinary Prolog, (2) using Prolog as the implementation language for an higher level of interpretation, and (3) extending Prolog with suitable features and directly using it.In this paper, we define the three uses in more details, compare them, and cite some concrete examples.

Using Prolog for building frog, a hybird knowledge representation system

FROG (FRames in ProlOG) is a Prolog based hybrid knowledge representation system which combines frames, production rules and Prolog at various levels. In this paper we shall first describe the particular technique we used for buiding the FROG system in Prolog. This technique is based on the use of apreprocessor which is able to produce the effective Prolog implementation of the system from an appropriate high level description of the knowledge of a given domain. We shall then describe the main features of the FROG system. The system supplies the knowledge engineer with a veryflexible frame structure in which each frame can contain either slots or production rules (with various kinds of inference strategies) and gives the possibility of using Prolog procedures in various places within each frame. Some hints on the Prolog implementation will also be given. Finally, the FROG high level language will be described. Both syntax and semantics of such a language are based on Prolog, thus assuring a uniform and precise description of a knowledge base. The language also allows control strategies in the system to be explicitly defined by the knowledge engineer.

Implementing Inference Strategies in Prolog Based Expert Systems

One of the most controversial question about the suitability of Prolog for implementing expert systems is its built-in, non-modifiable control strategy. In this paper a technique for implementing different inference strategies in Prolog is described, based on the use of suitable preprocessing facilities. Various inference mechanisms are implemented as Prolog programs which are generated automatically through preprocessing. It is argued that this approach has several advantages over other more traditional uses of Prolog in the implementation of expert systems. Implementations of forward and backward inference mechanisms, both with and without uncertainties are presented. Also an explanation mechanism and a technique for user level program debugging are suggested.