Towards a logic programming methodology based on higher-order predicates

This paper outlines a logic programming methodology which applies standardized logic program recursion forms afforded by a system of general purpose recursion schemes. The recursion schemes are conceived of as quasi higher-order predicates which accept predicate arguments, thereby representing parameterized program modules. This use of higher-order predicates is analogous to higher-order functionals in functional programming. However, these quasi higher-order predicates are handled by a metalogic programming technique within ordinary logic programming. Some of the proposed recursion operators are actualizations of mathematical induction principles (e.g. structural induction as generalization of primitive recursion). Others are heuristic schemes for commonly occurring recursive program forms. The intention is to handle all recursions in logic programs through the given repertoire of higher-order predicates. We carry out a pragmatic feasibility study of the proposed recursion operators with respect to the corpus of common textbook logic programs. This pragmatic investigation is accompanied with an analysis of the theoretical expressivity. The main theoretical results concerning computability are

1. Primitive recursive functions can be re-expressed in logic programming by predicates defined solely by non-recursive clauses augmented with afold recursion predicate akin to the fold operators in functional programming.
2. General recursive functions can be re-expressed likewise sincefold allows re-expression of alinrec recursion predicate facilitating linear, unbounded recursion.

     

Towards resource handling in logic programming: The PPL framework and fits semantics

The PPL framework is proposed as a simple extension to logic programming aiming at handling resources. It is argued that the separation between logical treatments and resource handling is desirable and, to that end, resources are proposed to be manipulated by means of pre- and post-conditions associated with usual Horn clauses. The expressiveness of the resulting framework is evidenced through the coding of several applications. Operational and declarative semantics are also presented as extensions of the classical ones accounting for the evaluation of pre- and post-conditions, in particular the non-monotonic behavior of the world of resources they induce in general.     

Multimodal logic programming

We give a framework for developing the least model semantics, fixpoint semantics, and SLD-resolution calculi for logic programs in multimodal logics whose frame restrictions consist of the conditions of seriality (i.e. ) and some classical first-order Horn clauses. Our approach is direct and no special restriction on occurrences of □_i and _i is required. We apply our framework for a large class of basic serial multimodal logics, which are parameterized by an arbitrary combination of generalized versions of axioms T, B, 4, 5 (in the form, e.g. 4:□_i→□_j□_k) and I:□_i→□_j. Another part of the work is devoted to programming in multimodal logics intended for reasoning about multidegree belief, for use in distributed systems of belief, or for reasoning about epistemic states of agents in multiagent systems. For that we also use the framework, and although these latter logics belong to the mentioned class of basic serial multimodal logics, the special SLD-resolution calculi proposed for them are more efficient.     

Probabilistic logic programming

Of all scientific investigations into reasoning with uncertainty and chance, probability theory is perhaps the best understood paradigm. Nevertheless, all studies conducted thus far into the semantics of quantitative logic programming have restricted themselves to non-probabilistic semantic characterizations. In this paper, we take a few steps towards rectifying this situation. We define a logic programming language that is syntactically similar to the annotated logics of Blair et al., 1987 and Blair and Subrahmanian, 1988, 45–73) but in which the truth values are interpreted probabilistically. A probabilistic model theory and fixpoint theory is developed for such programs. This probabilistic model theory satisfies the requirements proposed by Fenstad (in “Studies in Inductive Logic and Probabilities” (R. C. Jeffrey, Ed.), Vol. 2, pp. 251–262, Univ. of California Press, Berkeley, 1980) for a function to be called probabilistic. The logical treatment of probabilities is complicated by two facts: first, that the connectives cannot be interpreted truth-functionally when truth values are regarded as probabilities; second, that negation-free definite-clause-like sentences can be inconsistent when interpreted probabilistically. We address these issues here and propose a formalism for probabilistic reasoning in logic programming. To our knowledge, this is the first probabilistic characterization of logic programming semantics.     

Autoepistemic logic programming

An autoepistemic logic programming language is derived from a subset of a three-valued autoepistemic logic, called 3AEL. Autoepistemic programs generalize several ideas underlying logic programming: stable, supported, and well-founded models, Fitting's semantics, Kunen's semantics, and abductive frameworks can all be captured through simple autoepistemic translations; moreover, SLDNF-resolution and a generate-and-test method for stable semantics are generalized to provide sound and complete proof methods for autoepistemic programs. These methods extend existing proof methods for 3AEL. Thus autoepistemic logic programming, besides contributing to the understanding of 3AEL, can be seen as a unifying framework for the theory of logic programs. It should also be regarded as a first step toward a flexible environment where different forms of inference can be formally integrated.This paper is an extended version of [8]. I am grateful to my advisor, Giorgio Levi, to Paolo Mancarella, who read the first version of the paper, and to the anonymous referees, whose comments led to sensible improvements.     

Support logic programming

This article describes a support logic programming system which uses a theory of support pairs to model various forms of uncertainty. It should find application to designing expert systems and is of a query language type like Prolog. Uncertainty associated with facts and rules is represented by a pair of supports and uses ideas from Zadeh's fuzzy set theory and Shafer's evidence theory. A calculus is derived for such a system and various models of interpretation given. the article provides a form of knowledge representation and inference under uncertainty suitable for expert systems and a closed world assumption is not assumed. Facts not in the knowledge base are uncertain rather than assumed to be false.     

Relevant logic programming

In this paper we present a fragment of (positive) relevant logic which can be computed by a straightforward extension to SLD resolution while allowing full nesting of implications. These two requirements lead quite naturally to a fragment in which the major feature is an ambiguous user-level conjunction which is interpreted intensionally in query positions and extensionally in assertion positions. These restrictions allow a simple and efficient extension to SLD resolution (and more particularly, the PROLOG evaluation scheme) with quite minor loss in expressive power.     

Inductive logic programming

A new research area, Inductive Logic Programming, is presently emerging. While inheriting various positive characteristics of the parent subjects of Logic Programming and Machine Learning, it is hoped that the new area will overcome many of the limitations of its forebears. The background to present developments within this area is discussed and various goals and aspirations for the increasing body of researchers are identified. Inductive Logic Programming needs to be based on sound principles from both Logic and Statistics. On the side of statistical justification of hypotheses we discuss the possible relationship between Algorithmic Complexity theory and Probably-Approximately-Correct (PAC) Learning. In terms of logic we provide a unifying framework for Muggleton and Buntine’s Inverse Resolution (IR) and Plotkin’s Relative Least General Generalisation (RLGG) by rederiving RLGG in terms of IR. This leads to a discussion of the feasibility of extending the RLGG framework to allow for the invention of new predicates, previously discussed only within the context of IR.     

Nonmonotonic logic programming

This paper provides a survey of the state of the art in nonmonotonic logic programming. In particular, it surveys advances in the declarative semantics of logic programs, in query processing procedures for nonmonotonic logic programs, and in recent extensions of the nonmonotonic logic programming paradigm     

Symmetric structure in logic programming

It is argued that some symmetric structure in logic programs could be taken into account when implementing semantics in logic programming. This may enhance the declarative ability or expressive power of the semantics. The work presented here may be seen as representative examples along this line. The focus is on the derivation of negative information and some other classic semantic issues. We first define a permutation group associated with a given logic program. Since usually the canonical models used to reflect the common sense or intended meaning are minimal or completed models of the program, we expose the relationships between minimal models and completed models of the original program and its so-called G-reduced form newly-derived via the permutation group defined. By means of this G-reduced form, we introduce a rule to assume negative information termed G-CWA, which is actually a generalization of the GCWA. We also develop the notions of G-definite, G-hierarchical and G-stratified logic programs,     

Towards an efficient evaluation of recursive aggregates in deductive databases

This paper is devoted to the evaluation of aggregates (avg, sum,…) in deductive databases. Aggregates have proved to be a necessary modeling tool for a wide range of applications in non-deductive relational databases. They also appear to be important in connection with recursive rules, as shown by thebill of materials example. Several recent papers have studied the problem of semantics for aggregate programs. As in these papers, we distinguish between the classes of stratified (non-recursive) and recursive aggregate programs. For each of these two classes, the declarative semantics is recalled and an efficient evaluation algorithm is presented. The semantics and computation of aggregate programs in the recursive case are more complex: we rely on the notion of graph traversal to motivate the semantics and the evaluation method proposed. The algorithms presented here are integrated in the QSQ framework. Our work extends the recent work on aggregates by proposing an efficient algorithm in the recursive case. Recursive aggregates have been implemented in the EKS-V1 system.     

Temporal disjunctive logic programming

In this paper we introduce the logic programming languageDisjunctive Chronolog which combines the programming paradigms of temporal and disjunctive logic programming. Disjunctive Chronolog is capable of expressing dynamic behaviour as well as uncertainty, two notions that are very common in a variety of real systems. We present the minimal temporal model semantics and the fixpoint semantics for the new programming language and demonstrate their equivalence. We also show how proof procedures developed for disjunctive logic programs can be easily extended to apply to Disjunctive Chronolog programs. Manolis Gergatsoulis, Ph.D.: He received his B.Sc. in Physics in 1983, the M.Sc. and the Ph.D. degrees in Computer Science in 1986 and 1995 respectively all from the University of Athens, Greece. Since 1996 he is a Research Associate in the Institute of Informatics and Telecommunications, NCSR ‘Demokritos’, Athens. His research interests include logic and temporal programming, program transformations and synthesis, as well as theory of programming languages. Panagiotis Rondogiannis, Ph.D.: He received his B.Sc. from the Department of Computer Engineering and Informatics, University of Patras, Greece, in 1989, and his M.Sc. and Ph.D. from the Department of Computer Science, University of Victoria, Canada, in 1991 and 1994 respectively. From 1995 to 1996 he served in the Greek army. From 1996 to 1997 he was a visiting professor in the Department of Computer Science, University of Ioannina, Greece, and since 1997 he is a Lecturer in the same Department. In January 2000 he was elected Assistant Professor in the Department of Informatics at the University of Athens. His research interests include functional, logic and temporal programming, as well as theory of programming languages. Themis Panayiotopoulos, Ph.D.: He received his Diploma on Electrical Engineering from the Department of Electrical Engineering, National Technical Univesity of Athens, in 1984, and his Ph.D. on Artificial Intelligence from the above mentioned department in 1989. From 1991 to 1994 he was a visiting professor at the Department of Mathematics, University of the Aegean, Samos, Greece and a Research Associate at the Institute of Informatics and Telecommunications of “Democritos” National Research Center. Since 1995 he is an Assistant Prof. at the Department of Computer Science, University of Piraeus. His research interests include temporal programming, logic programming, expert systems and intelligent agent architectures.     

Beyond multi-adjoint logic programming

In ‘multi-adjoint logic programming’, MALP in brief, each fuzzy logic program is associated with its own ‘multi-adjoint lattice’ for modelling truth degrees beyond the simpler case of true and false, where a large set of fuzzy connectives can be defined. On this wide repertoire, it is crucial to connect each implication symbol with a proper conjunction thus conforming constructs of the form (←_i, &_i) called ‘adjoint pairs’, whose use directly affects both declarative and operational semantics of the MALP framework. In this work, we firstly show how the strong dependence of adjoint pairs can be largely weakened for an interesting ‘sub-class’ of MALP programs. Then, we reason in a similar way till conceiving a ‘super-class’ of fuzzy logic programs beyond MALP, which definitively drops out the need for using adjoint pairs, since the new semantics behaviour relies on much more relaxed lattices than multi-adjoint ones.     

Neural fuzzy logic programming

A foundational development of propositional fuzzy logic programs is presented. Fuzzy logic programs are structured knowledge bases including uncertainties in rules and facts. The precise specifications of uncertainties have a great influence on the performance of the knowledge base. It is shown how fuzzy logic programs can be transformed to neural networks, where adaptations of uncertainties in the knowledge base increase the reliability of the program and are carried out automatically.