Jack Minker explains, in his accompanying Editor's Introduction, the circumstances which have brought about the founding of this new journal, the Theory and Practice of Logic Programming (TPLP). The birth of any new journal is an occasion for rejoicing, but in the present case the logic programming community has special reasons to be proud and grateful.

We should be proud that TPLP is published by the Cambridge University Press. The Press in Cambridge is the oldest, and many (I am one) believe it is by far the best, university press in the world. In joining its lists the TPLP immediately acquires some of the patina of centuries of glorious academic excellence and devotion to scholarship. It is a splendid thing – an honor, indeed – to be associated with a publishing house which has for so long and so brilliantly served the academic community without in any way needing to compromise academic values in achieving commercial success.

We should be grateful to our colleagues Krzysztof Apt and Maurice Bruynooghe, who have worked so hard and with such selfless dedication to provide the logic programming community with a journal that it can continue to sponsor. Sadly, this is no longer the case with respect to Journal of Logic Programming. In 1984 when I decided as Founding Editor to publish JLP with the Elsevier organization, the arrangement was satisfactory to both parties, and continued to be so for many years. However, more recently, with the increasing pressures being put on library budgets, the worlds of academic and commercial publishers seem to have grown apart to the point where incompatibilities outweigh affinities. The logic programming community has now entered into a far more solid relationship. The logic of the new marriage is based much more on solid assessment of the interests of both parties. We are living through times of important change in the publishing world, and the new order is taking shape along intelligible and stable lines.

All is well. Although it is with sadness that we bid goodbye to the JLP, we must reflect that this is only a transmigration of one and the same living entity, from an old incarnation to a new one. So it is with joy and hope that we at the same time welcome the Theory and Practice of Logic Programming. The JLP had a good life, and its great spirit will live on in TPLP. The continuity of the logic programming community's research record is unbroken and unscathed. Its future is now secure in the care of the best of all possible editorial leadership, and under the banner of the best of all possible publishers.

The publication of this first issue of the Theory and Practice of Logic Programming (TPLP) is an important milestone in the history of logic programming. The field of logic programming started in the early 1970s and is based on the seminal work of J. Alan Robinson who developed the well-known Robinson resolution principle that revolutioned the field of automated reasoning and upon which logic programming is based. The field of logic programming was created by Alain Colmerauer and Robert Kowalski in the early 1970sIn addition to Colmerauer and Kowalski, others had conceived of similar concepts at about the same time: Ted Elcock developed a system ABSET, which was basically a logic programming system and Carl Hewitt developed Planner, which although advertised as a procedural-oriented system, had a declarative component.. Alain Colmerauer and his group developed the first logic programming language, Prolog (PROgrammation en LOGique).

Robert Kowalski modified the resolution principle by limiting it to Horn clauses and by extending it to a formalism amenable for computing. His work with van Emden provided semantic foundations to this approach to programming. The importance of logic programming is that it is a declarative rather than a procedural programming language.

We propose a framework for modeling uncertainty where both belief and doubt can be given independent, first-class status. We adopt probability theory as the mathematical formalism for manipulating uncertainty. An agent can express the uncertainty in her knowledge about a piece of information in the form of a confidence level, consisting of a pair of intervals of probability, one for each of her belief and doubt. The space of confidence levels naturally leads to the notion of a trilattice, similar in spirit to Fitting's bilattices. Intuitively, the points in such a trilattice can be ordered according to truth, information, or precision. We develop a framework for probabilistic deductive databases by associating confidence levels with the facts and rules of a classical deductive database. While the trilattice structure offers a variety of choices for defining the semantics of probabilistic deductive databases, our choice of semantics is based on the truth-ordering, which we find to be closest to the classical framework for deductive databases. In addition to proposing a declarative semantics based on valuations and an equivalent semantics based on fixpoint theory, we also propose a proof procedure and prove it sound and complete. We show that while classical Datalog query programs have a polynomial time data complexity, certain query programs in the probabilistic deductive database framework do not even terminate on some input databases. We identify a large natural class of query programs of practical interest in our framework, and show that programs in this class possess polynomial time data complexity, i.e. not only do they terminate on every input database, they are guaranteed to do so in a number of steps polynomial in the input database size.

In this paper we analyze declarative deterministic and non-deterministic semantics for active rules. In particular, we consider several (partial) stable model semantics, previously defined for deductive rules, such as well-founded, max deterministic, unique total stable model, total stable model and maximal stable model semantics. The semantics of an active program [Ascr ][Pscr ] is given by first rewriting it into a deductive program [Lscr ][Pscr ], then computing a model M defining the declarative semantics of [Lscr ][Pscr ] and, finally, applying ‘consistent’ updates contained in M to the source database. The framework we propose permits a natural integration of deductive and active rules and can also be applied to queries with function symbols or to queries over infinite databases.

Infinite loops and redundant computations are long recognized open problems in Prolog. Two methods have been explored to resolve these problems: loop checking and tabling. Loop checking can cut infinite loops, but it cannot be both sound and complete even for function-free logic programs. Tabling seems to be an effective way to resolve infinite loops and redundant computations. However, existing tabulated resolutions, such as OLDT-resolution, SLG-resolution and Tabulated SLS-resolution, are non-linear because they rely on the solution-lookup mode in formulating tabling. The principal disadvantage of non-linear resolutions is that they cannot be implemented using a simple stack-based memory structure like that in Prolog. Moreover, some strictly sequential operators such as cuts may not be handled as easily as in Prolog. In this paper, we propose a hybrid method to resolve infinite loops and redundant computations. We combine the ideas of loop checking and tabling to establish a linear tabulated resolution called TP-resolution. TP-resolution has two distinctive features: (1) it makes linear tabulated derivations in the same way as Prolog except that infinite loops are broken and redundant computations are reduced. It handles cuts as effectively as Prolog; and (2) it is sound and complete for positive logic programs with the bounded-term-size property. The underlying algorithm can be implemented by an extension to any existing Prolog abstract machines such as WAM or ATOAM.

U-Datalog has been developed with the aim of providing a set-oriented logical update language, guaranteeing update parallelism in the context of a Datalog-like language. In U-Datalog, updates are expressed by introducing constraints (+p(X), to denote insertion, and −p(X), to denote deletion) inside Datalog rules. A U-Datalog program can be interpreted as a CLP program. In this framework, a set of updates (constraints) is satisfiable if it does not represent an inconsistent theory, that is, it does not require the insertion and the deletion of the same fact. This approach resembles a very simple form of negation. However, on the other hand, U-Datalog does not provide any mechanism to explicitly deal with negative information, resulting in a language with limited expressive power. In this paper, we provide a semantics, based on stratification, handling the use of negated atoms in U-Datalog programs, and we show which problems arise in defining a compositional semantics.

Errors, like straws, upon the surface flow; He who would search for pearls must dive below.

– John Dryden

I am honoured to be the editor of the Logic Programming Pearls section of the journal Theory and Practice of Logic Programming. Below are some brief guidelines for submission of papers to this section. Note that submissions of pearls in logic programming languages other than Prolog are welcome. Functional programming language pearls, although similar in spirit, are more appropriately submitted to another journal such as the Journal of Functional Programming.

A programming pearl is a short piece of self-contained code of outstanding quality. Ideally it should be clearly correct, elegant, concise, efficient, etc., though in some cases a (small) subset of these may not apply. It may be a useful application or may primarily be an example of a useful programming technique. Portability is not so important in this context, but if it can be achieved without sacrificing other qualities, so much the better.

Accompanying text explains the code and its qualities. These may be exposed by describing how a programmer could derive the code. Ideally, a logic programming pearl should also showcase the logic programming paradigm, for example, declarative semantics, nondeterminism, logic variables, definite clause grammars, meta programming, and so forth.

The following classification may help clarify what is considered to be a logic programming pearl.