Autoepistemic logics as a unifying framework for the semantics of logic programs

In this paper, it is shown that a three-valued autoepistemic logic provides an elegant unifying framework for some of the major semantics of normal and disjunctive logic programs and logic programs with classical negation, namely, the stable semantics, the well-founded semantics, supported models, Fitting's semantics, Kunen's semantics, the stationary semantics, and answer sets. For the first time, so many semantics are embedded into one logic. The framework extends previous results—by Gelfond, Lifschitz, Marek, Subrahmanian, and Truszczynski —on the relationships between logic programming and Moore's autoepistemic logic. The framework suggests several new semantics for negation-as-failure. In particular, we will introduce the epistemic semantics for disjunctive logic programs. In order to motivate the epistemic semantics, an interesting class of applications called ignorance tests will be formalized; it will be proved that ignorance tests can be defined by means of the epistemic semantics, but not by means of the old semantics for disjunctive programs. The autoepistemic framework provides a formal foundation for an environment that integrates different forms of negation. The role of classical negation and various forms of negation-by-failure in logic programming will be briefly discussed.     

Autoepistemic logic of first order and its expressive power

We study the expressive power of first-order autoepistemic logic. We argue that full introspection of rational agents should be carried out by minimizing positive introspection and maximizing negative introspection. Based on full introspection, we propose the maximal well-founded semantics that characterizes autoepistemic reasoning processes of rational agents, and show that breadth of the semantics covers all theories in autoepistemic logic of first order, Moore's AE logic, and Reiter's default logic. Our study demonstrates that the autoepistemic logic of first order is a very powerful framework for nonmonotonic reasoning, logic programming, deductive databases, and knowledge representation.This research is partially supported by NSERC grant OGP42193.     

Uniform semantic treatment of default and autoepistemic logics

We revisit the issue of epistemological and semantic foundations for autoepistemic and default logics, two leading formalisms in nonmonotonic reasoning. We develop a general semantic approach to autoepistemic and default logics that is based on the notion of a belief pair and that exploits the lattice structure of the collection of all belief pairs. For each logic, we introduce a monotone operator on the lattice of belief pairs. We then show that a whole family of semantics can be defined in a systematic and principled way in terms of fixpoints of this operator (or as fixpoints of certain closely related operators). Our approach elucidates fundamental constructive principles in which agents form their belief sets, and leads to approximation semantics for autoepistemic and default logics. It also allows us to establish a precise one-to-one correspondence between the family of semantics for default logic and the family of semantics for autoepistemic logic. The correspondence exploits the modal interpretation of a default proposed by Konolige. Our results establish conclusively that default logic can be viewed as a fragment of autoepistemic logic, a result that has been long anticipated. At the same time, they explain the source of the difficulty to formally relate the semantics of default extensions by Reiter and autoepistemic expansions by Moore. These two semantics occupy different locations in the corresponding families of semantics for default and autoepistemic logics.     

Stable semantics for disjunctive programs

We introduce the stable model semantics fordisjunctive logic programs and deductive databases, which generalizes the stable model semantics, defined earlier for normal (i.e., non-disjunctive) programs. Depending on whether only total (2-valued) or all partial (3-valued) models are used we obtain thedisjunctive stable semantics or thepartial disjunctive stable semantics, respectively. The proposed semantics are shown to have the following properties:

? For normal programs, the disjunctive (respectively, partial disjunctive) stable semantics coincides with thestable (respectively,partial stable) semantics.

? For normal programs, the partial disjunctive stable semantics also coincides with thewell-founded semantics.

? For locally stratified disjunctive programs both (total and partial) disjunctive stable semantics coincide with theperfect model semantics.

? The partial disjunctive stable semantics can be generalized to the class ofall disjunctive logic programs.

? Both (total and partial) disjunctive stable semantics can be naturally extended to a broader class of disjunctive programs that permit the use ofclassical negation.

? After translation of the programP into a suitable autoepistemic theory \( \hat P \) the disjunctive (respectively, partial disjunctive) stable semantics ofP coincides with the autoepistemic (respectively, 3-valued autoepistemic) semantics of \( \hat P \) .

     

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.     

On the autoepistemic reconstruction of logic programming

Current semantics of logic programs normally ignore thesyntactical aspects of the programs. As a result, only the meanings ofsome well-behaved programs can be captured by these semantics. In this paper however, we propose a new semantics of logic programs that can reflectsome of the syntactical behaviours of the programs. The central notion of the semantics is the concept of aneutral clause p ← A which does not affect the behaviour of p in a program. The logic that underlies the semantics is based on anintensional extension of Levesque’s autoepistemicpredicate logic. It differs from existing autoepistemic logics in that it isquantificational andconstructive. We will also compare and contrast our semantics with some well-known semantics. In particular, we will show how to capture the undefined value of a logic program without resorting to a three-valued nonmonotonic formalism. This is achieved by translating an incoherent AE logic program to a program with multiple AE extensions whose intersection can then be used to characterize the undefined value of a logic program.     

Well-founded and stationary models of logic programs

We investigate the class ofstationary or partial stable models of normal logic programs. This important class of models includes all (total)stable models, and, moreover, thewell-founded model is always its smallest member. Stationary models have several natural fixed-point definitions and can be equivalently obtained as expansions or extensions of suitable autoepistemic or default theories. By taking a particular subclass of this class of models one can obtain different semantics of logic programs, including the stable semantics and the well-founded semantics. Stationary models can be also naturally extended to the class of all disjunctive logic programs. These features of stationary models designate them as an important class of models with applications reaching far beyond the realm of logic programming.Partially supported by the National Science Foundation grant #IRI-9313061.     

Hyperequivalence of logic programs with respect to supported models

Recent research in nonmonotonic logic programming has focused on certain types of program equivalence, which we refer to here as hyperequivalence, that are relevant for program optimization and modular programming. So far, most results concern hyperequivalence relative to the stable-model semantics. However, other semantics for logic programs are also of interest, especially the semantics of supported models which, when properly generalized, is closely related to the autoepistemic logic of Moore. In this paper, we consider a family of hyperequivalence relations for programs based on the semantics of supported and supported minimal models. We characterize these relations in model-theoretic terms. We use the characterizations to derive complexity results concerning testing whether two programs are hyperequivalent relative to supported and supported minimal models.     

Operational Concepts of Nonmonotonic Logics Part 2: Autoepistemic Logic

The subject of nonmonotonic reasoning is reasoning with incompleteinformation. One of the main approaches is autoepistemic logic inwhich reasoning is based on introspection. This paper aims at providing a smooth introduction to this logic,stressing its motivation and basic concepts. The meaning (semantics)of autoepistemic logic is given in terms of so-called expansionswhich are usually defined as solutions of a fixed-point equation. Thepresent paper shows a more understandable, operational method fordetermining expansions. By improving applicability of the basicconcepts to concrete examples, we hope to make a contribution to awider usage of autoepistemic logic in practical applications.     

Partial equilibrium logic

Partial equilibrium logic (PEL) is a new nonmonotonic reasoning formalism closely aligned with logic programming under well-founded and partial stable model semantics. In particular it provides a logical foundation for these semantics as well as an extension of the basic syntax of logic programs. In this paper we describe PEL, study some of its logical properties and examine its behaviour on disjunctive and nested logic programs. In addition we consider computational features of PEL and study different approaches to its computation.     

Iterative belief revision in extended logic programming

Extended logic programming augments conventional logic programming with both default and explicit negation. Several semantics for extended logic programs have been proposed that extend the well-founded semantics for logic programs with default negation (called normal programs). We show that two of these extended semantics are intractable; both Dung's grounded argumentation semantics and the well-founded semantics of Alferes et al. are NP-hard. Nevertheless, we also show that these two semantics have a common core, a more restricted form of the grounded semantics, which is tractable and can be computed iteratively in quadratic time. Moreover, this semantics is a representative of a rich class of tractable semantics based on a notion of iterative belief revision.     

Making default inferences from logic programs

The relationship between Reiter's default logic and general logic programs, which may contain negative subgoals in rule bodies, has been discussed in the literature by translating logic programs to default logic theories. Here, we present a method to translate some default logic theories to general logic programs, and study the extensions of default logic theories with the stable model semantics of logic programming. Based on the translation method, we show that another semantics of logic programming, the well-founded semantics, can be used to define a new version of default logic, which is more cautious than the original one. This enables the existing proof procedures for the well-founded semantics to perform default inference. We also study the property of cumulative monotonicity for both default logic theories and general logic programs under the two different semantics. As a direct application of the translation method, logic programs can be used to make default inference for semantic networks with exceptions. La relation entre la logique par défaut de Reiter et les programmes logiques généraux, qui peuvent comporter des sous-buts négatifs dans les ensembles de règies, a déjàété discutée en traduisant les programmes logiques en théories de la logique par défaut. Dans cet article, les auteurs proposent une méthode pour traduire certaines théories de la logique par defaut en programmes logiques généraux et étudient les extensions des theories de la logique par defaut avec la sémantique de modéle stable de la programmation logique. En se basant sur la méthode de traduction, les auteurs démontrent qu'une autre sémantique de la programmation logique, la sémantique bien fondée, peut ětre utilisée pour définir une nouvelle version de la logique par défaut, qui est plus prudente que la première. Cela permet aux procédures de preuve existantes de la sémantique bien fondée d' effectuer l' inférence par défaut. Les auteurs examinent également les caracteristiques de la monotonicité cumulative pour les theories de la logique par défaut et les programmes logiques généraux selon les deux différentes sémantiques. Une application directe de la méthode de traduction est la possibilityé d' utiliser les programmes logiques pour effectuer de l' inférence par défaut pour les réseaux sémantiques avec exceptions.     

On the computational cost of disjunctive logic programming: Propositional case

This paper addresses complexity issues for important problems arising with disjunctive logic programming. In particular, the complexity of deciding whether a disjunctive logic program is consistent is investigated for a variety of well-known semantics, as well as the complexity of deciding whether a propositional formula is satisfied by all models according to a given semantics. We concentrate on finite propositional disjunctive programs with as well as without integrity constraints, i.e., clauses with empty heads; the problems are located in appropriate slots of the polynomial hierarchy. In particular, we show that the consistency check is ₂ ^p -complete for the disjunctive stable model semantics (in the total as well as partial version), the iterated closed world assumption, and the perfect model semantics, and we show that the inference problem for these semantics is ₂ ^p -complete; analogous results are derived for the answer sets semantics of extended disjunctive logic programs. Besides, we generalize previously derived complexity results for the generalized closed world assumption and other more sophisticated variants of the closed world assumption. Furthermore, we use the close ties between the logic programming framework and other nonmonotonic formalisms to provide new complexity results for disjunctive default theories and disjunctive autoepistemic literal theories.Parts of the results in this paper appeared in form of an abstract in the Proceedings of the Twelfth ACM SIGACT SIGMOD-SIGART Symposium on Principles of Database Systems (PODS-93), pp. 158–167. Other parts appeared in shortened form in the Proceedings of the International Logic Programming Symposium, Vancouver, October 1993 (ILPS-93), pp. 266–278. MIT Press.     

Computation of stable models and its integration with logical queryprocessing

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     

A new approach to hybrid probabilistic logic programs

This paper presents a novel revision of the framework of Hybrid Probabilistic Logic Programming, along with a complete semantics characterization, to enable the encoding of and reasoning about real-world applications. The language of Hybrid Probabilistic Logic Programs framework is extended to allow the use of non-monotonic negation, and two alternative semantical characterizations are defined: stable probabilistic model semantics and probabilistic well-founded semantics. These semantics generalize the stable model semantics and well-founded semantics of traditional normal logic programs, and they reduce to the semantics of Hybrid Probabilistic Logic programs for programs without negation. It is the first time that two different semantics for Hybrid Probabilistic Programs with non-monotonic negation as well as their relationships are described. This proposal provides the foundational grounds for developing computational methods for implementing the proposed semantics. Furthermore, it makes it clearer how to characterize non-monotonic negation in probabilistic logic programming frameworks for commonsense reasoning. An erratum to this article can be found at