Unlike SLD resolution as implemented in Prolog, tabled evaluation with delaying guarantees termination for function free logic programs, avoids repeated computation of identical subqueries, and handles recursion through negation. It is often used in query processing and nonmonotonic reasoning where termination is required. The paper presents a new technique for incorporating tabled evaluation into existing Prolog systems. It requires neither time consuming modifications of a Prolog engine nor metainterpretation that can enormously slow down program execution. Instead, using a program transformation approach, the technique allows effective use of the advanced Prolog technology. The transformed program uses tabling primitives implemented externally in C that provide direct control over the search strategies. This brings efficiency as well as portability across Prolog systems. Experiences with a prototype implementation indicate that the approach results in a flexible and pragmatic method for query processing and nonmonotonic reasoning on top of Prolog. Performance measurements show that the method is efficient for practical applications.

SLD resolution with negation as finite failure (SLDNF) reflects the procedural interpretation of predicate calculus as a programming language and forms the computational basis for Prolog systems. Despite its advantages for stack-based memory management, SLDNF is often not appropriate for query evaluation for three reasons: (a) it may not terminate due to infinite positive recursion; (b) it may be terminate due to infinite recursion through negation; and (c) it may repeatedly evaluate the same literal in a rule body, leading to unacceptable performance. We address all three problems for goal-oriented query evaluation of general logic programs by presenting tabled evaluation with delaying, called SLG resolution. It has three distinctive features: (i) SLG resolution is a partial deduction procedure, consisting of seven fundamental transformations. A query is transformed step by step into a set of answers. The use of transformations separates logical issues of query evaluation from procedural ones. SLG allows an arbitrary computation rule for selecting a literal from a rule body and an arbitrary control strategy for selecting transformations to apply. (ii) SLG resolution is sound and search space complete with respect to the well-founded partial model for all non-floundering queries, and preserves all three-valued stable models. To evaluate a query under differenc three-valued stable models, SLG resolution can be enhanced by further processing of the answers of subgoals relevant to a query. (iii) SLG resolution avoids both positive and negative loops and always terminates for programs with the bounded-term-size property. It has a polynomial time data complexity for well-founded negation of function-free programs. Through a delaying mechanism for handling ground negative literals involved in loops, SLG resolution avoids the repetition of any of its derivation steps. Restricted forms of SLG resolution are identified for definite, locally stratified, and modularly stratified programs, shedding light on the role each transformation plays.

The well-founded semantics and the stable model semantics capture intuitions of the skeptical and credulous semantics in nonmonotonic reasoning, respectively. They represent the two dominant proposals for the declarative semantics of deductive databases and logic programs. However, neither semantics seems to be suitable for all applications. We have developed an efficient implementation of goal-oriented effective query evaluation under the well-founded semantics. It produces a residual program for subgoals that are relevant to a query, which contains facts for true instances and clauses with body literals for undefined instances. We present a simple method of stable model computation that can be applied to the residual program of a query to derive answers with respect to stable models. The method incorporates both forward and backward chaining to propagate the assumed truth values of ground atoms, and derives multiple stable models through backtracking. Users are able to request that only stable models satisfying certain conditions be computed. A prototype has been developed that provides integrated query evaluation under the well-founded semantics, the stable models, and ordinary Prolog execution. We describe the user interface of the prototype and present some experimental results.

SLD resolution with negation as finite failure (or SLDNF) reflects the procedural interpretation of Horn-clause predicate logic as a programming language and forms the computational basis for prolog systems. Despite its advantages in memory management, SLDNF is often not appropriate for query evaluation for three reasons; a) it may not terminate due to infinite positive recursion; b) it may not terminate due to infinite recursion through negation; and c) it may repeatedly evaluate the same clause body literal, leading to unacceptable performance.

A view of the current hypertext technology and how it can be greatly aided through the use of artificial intelligence, especially expert systems shells, is provided. The proposed hypertext technology will contribute to the development of more advanced and sophisticated hypertext documents which can support built-in intelligence with memory retention capabilities.>

Our objective is to have a logical framework for natural representation and manipulation of complex objects. We start with an analysis of semantic modeling of complex objects, and attempt to understand what are the fundamental aspects which need to be captured. A logic, called C-logic, is then presented which provides direct support for what we believe to be basic features of complex objects, including object identity, multi-valued labels and a dynamic notion of types. C-logic has a simple first-order semantics, but it also allows natural specification of complex objects and gives us a framework for exploring efficient logic deduction over complex objects.