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.     

Abstract interpretation of resolution-based semantics

We extend the abstract interpretation point of view on context-free grammars by Cousot and Cousot to resolution-based logic programs and proof systems. Starting from a transition-based small-step operational semantics of Prolog programs (akin to the Warren Machine), we consider maximal finite derivations for the transition system from most general goals. This semantics is abstracted by instantiation to terms and furthermore to ground terms, following the so-called c- and s-semantics approach. Orthogonally, these sets of derivations can be abstracted to SLD-trees, call patterns and models, as well as interpreters providing effective implementations (such as Prolog). These semantics can be presented in bottom–up fixpoint form. This abstract interpretation-based construction leads to classical bottom–up semantics (such as the s-semantics of computed answers, the c-semantics of correct answers of Keith Clark, and the minimal-model semantics of logical consequences of Maarten van Emden and Robert Kowalski). The approach is general and can be applied to infinite and top–down semantics in a straightforward way.     

Disjunctive Stable Models: Unfounded Sets, Fixpoint Semantics, and Computation

Disjunctive logic programs have become a powerful tool in knowledge representation and commonsense reasoning. This paper focuses on stable model semantics, currently the most widely acknowledged semantics for disjunctive logic programs. After presenting a new notion of unfounded sets for disjunctive logic programs, we provide two declarative characterizations of stable models in terms of unfounded sets. One shows that the set of stable models coincides with the family of unfounded-free models (i.e., a model is stable iff it contains no unfounded atoms). The other proves that stable models can be defined equivalently by a property of their false literals, as a model is stable iff the set of its false literals coincides with its greatest unfounded set. We then generalize the well-founded operator to disjunctive logic programs, give a fixpoint semantics for disjunctive stable models and present an algorithm for computing the stable models of function-free programs. The algorithm's soundness and completeness are proved and some complexity issues are discussed.     

Logical approximation for program analysis

The abstract interpretation of programs relates the exact semantics of a programming language to a finite approximation of those semantics. In this article, we describe an approach to abstract interpretation that is based in logic and logic programming. Our approach consists of faithfully representing a transition system within logic and then manipulating this initial specification to create a logical approximation of the original specification. The objective is to derive a logical approximation that can be interpreted as a terminating forward-chaining logic program; this ensures that the approximation is finite and that, furthermore, an appropriate logic programming interpreter can implement the derived approximation. We are particularly interested in the specification of the operational semantics of programming languages in ordered logic, a technique we call substructural operational semantics (SSOS). We show that manifestly sound control flow and alias analyses can be derived as logical approximations of the substructural operational semantics of relevant languages.     

Logic programs with abstract constraint atoms: The role of computations

We provide a new perspective on the semantics of logic programs with arbitrary abstract constraints. To this end, we introduce several notions of computation. We use the results of computations to specify answer sets of programs with constraints. We present the rationale behind the classes of computations we consider, and discuss the relationships among them. We also discuss the relationships among the corresponding concepts of answer sets. One of those concepts has several compelling characterizations and properties, and we propose it as the correct generalization of the answer-set semantics to the case of programs with arbitrary constraints. We show that several other notions of an answer set proposed in the literature for programs with constraints can be obtained within our framework as the results of appropriately selected classes of computations.     

Contributions to the semantics of logic perpetual processes

Summary Logic perpetual processes (logic programs with infinite data structures) have been given several formal (operational and fixpoint) semantics. In this paper, we compare the various semantics and define a formal characterization of a least fixpoint semantics, which is based on a modified version of the logic programs and which is satisfactory for a large class of logical perpetual processes. Our results show that all the proposed fixpoint semantics are not equivalent to the operational semantics and suggest an improvement of the least fixpoint approach.     

A new universal approximation result for fuzzy systems,which reflects CNF DNF duality

There are two main fuzzy system methodologies for translating expert rules into a logical formula: In Mamdani's methodology, we get a DNF formula (disjunction of conjunctions), and in a methodology which uses logical implications, we get, in effect, a CNF formula (conjunction of disjunctions). For both methodologies, universal approximation results have been proven which produce, for each approximated function f(x), two different approximating relations R_DNF(x, y) and R_CNF(x, y). Since, in fuzzy logic, there is a known relation F_CNF(x) ≤ F_DNF(x) between CNF and DNF forms of a propositional formula F, it is reasonable to expect that we would be able to prove the existence of approximations for which a similar relation R_CNF(x, y) ≤ R_DNF(x, y) holds. Such existence is proved in our paper. © 2002 Wiley Periodicals, Inc.     

Consistency properties and set based logic programming

Blair et al. (2001) developed an extension of logic programming called set based logic programming. In the theory of set based logic programming the atoms represent subsets of a fixed universe X and one is allowed to compose the one-step consequence operator with a monotonic idempotent operator O so as to ensure that the analogue of stable models in the theory are always closed under O. Marek et al. (1992, Ann Pure Appl Logic 96:231–276 1999) developed a generalization of Reiter’s normal default theories that can be applied to both default theories and logic programs which is based on an underlying consistency property. In this paper, we show how to extend the normal logic programming paradigm of Marek, Nerode, and Remmel to set based logic programming. We also show how one can obtain a new semantics for set based logic programming based on a consistency property.     

The globally Bi-3-connected property of the honeycomb rectangular torus

The honeycomb rectangular torus is an attractive alternative to existing networks such as mesh-connected networks in parallel and distributed applications because of its low network cost and well-structured connectivity. Assume that m and n are positive even integers with n ? 4. It is known that every honeycomb rectangular torus HReT(m,n) is a 3-regular bipartite graph. We prove that in any HReT(m,n), there exist three internally-disjoint spanning paths joining x and y whenever x and y belong to different partite sets. Moreover, for any pair of vertices x and y in the same partite set, there exists a vertex z in the partite set not containing x and y, such that there exist three internally-disjoint spanning paths of G-{z} joining x and y. Furthermore, for any three vertices x, y, and z of the same partite set there exist three internally-disjoint spanning paths of G-{z} joining x and y if and only if n ? 6 or m = 2.     

A new generic scheme for functional logic programming with constraints

In this paper we propose a new generic scheme CFLP풟, intended as a logical and semantic framework for lazy Constraint Functional Logic Programming over a parametrically given constraint domain 풟. As in the case of the well known CLP풟 scheme for Constraint Logic Programming, 풟 is assumed to provide domain specific data values and constraints. CFLP풟 programs are presented as sets of constrained rewrite rules that define the behavior of possibly higher order and/or non-deterministic lazy functions over 풟. As a main novelty w.r.t. previous related work, we present a Constraint Rewriting Logic CRWL풟 which provides a declarative semantics for CFLP풟 programs. This logic relies on a new formalization of constraint domains and program interpretations, which allows a flexible combination of domain specific data values and user defined data constructors, as well as a functional view of constraints. This research has been partially supported by the Spanish National Projects MELODIAS (TIC2002-01167), MERIT-FORMS (TIN2005-09207-C03-03) and PROMESAS-CAM (S-0505/TIC/0407).     

Protected completions of first-order general logic programs

Minker and Perlis [15] have made the important observation that in certain circumstances, it might be desirable to prevent the inference of A when A is in the finite failure set of a logic program P. In this paper, we investigate the model-theoretic aspects of their proposal and develop a Fitting-style [5] declarative semantics for protected completions of general logic programs (containing function symbols). This extends the Minker-Perlis proposal which applies to function-free pure logic programs. In addition, an operational semantics is proposed and it is proven to be sound for existentially quantified positive queries and negative ground queries to general, canonical protected logic programs. Completeness issues are investigated and completeness is proved for positive existential queries and negative ground queries for the following classes of programs: (1) function-free general protected logic programs (the Minker-Perlis operational semantics apply to function-free pure protected logic programs), (2) pure protected logic programs (with function symbols) and (3) protected general logic programs that do not contain any internal variables (though they may contain function symbols).     

Lp, a logic for representing and reasoning with statistical knowledge

This paper presents a logical formalism for representing and reasoning with statistical knowledge. One of the key features of the formalism is its ability to deal with qualitative statistical information. It is argued that statistical knowledge, especially that of a qualitative nature, is an important component of our world knowledge and that such knowledge is used in many different reasoning tasks. The work is further motivated by the observation that previous formalisms for representing probabilistic information are inadequate for representing statistical knowledge. The representation mechanism takes the form of a logic that is capable of representing a wide variety of statistical knowledge, and that possesses an intuitive formal semantics based on the simple notions of sets of objects and probabilities defined over those sets. Furthermore, a proof theory is developed and is shown to be sound and complete. The formalism offers a perspicuous and powerful representational tool for statistical knowledge, and a proof theory which provides a formal specification for a wide class of deductive inferences. The specification provided by the proof theory subsumes most probabilistic inference procedures previously developed in AI. The formalism also subsumes ordinary first-order logic, offering a smooth integration of logical and statistical knowledge.     

On an approximate minimax circle closest to a set of points

We show how the Chebychev minimax criterion for finding a circle closest to a set of points can be approximated well by standard linear programming procedures.Scope and purposeProblems that arise in location theory and in the quality control of manufactured parts (drilled holes, shaped spheres) call for finding an annulus of minimum width that encompasses a set of points. For the two-dimensional case, this is equivalent to determining a closest “deviation” circle with center (x₀,y₀) and radius r₀ such that the maximum radial distance of the points to the circumference of the deviation circle is minimized. The required annulus (narrowest ring) is formed by two circles, centered at (x₀,y₀), that inscribe and circumscribe the given set of points. We suggest that our linear-programming procedure be used to approximate this annulus as, unlike exact methods, it is stable, fast, and generalizes readily to higher-dimensional point sets.     

Active logic semantics for a single agent in a static world

For some time we have been developing, and have had significant practical success with, a time-sensitive, contradiction-tolerant logical reasoning engine called the active logic machine (ALMA). The current paper details a semantics for a general version of the underlying logical formalism, active logic. Central to active logic are special rules controlling the inheritance of beliefs in general (and of beliefs about the current time in particular), very tight controls on what can be derived from direct contradictions ($P\mathrm{\&}\neg P$), and mechanisms allowing an agent to represent and reason about its own beliefs and past reasoning. Furthermore, inspired by the notion that until an agent notices that a set of beliefs is contradictory, that set seems consistent (and the agent therefore reasons with it as if it were consistent), we introduce an “apperception function” that represents an agent's limited awareness of its own beliefs, and serves to modify inconsistent belief sets so as to yield consistent sets. Using these ideas, we introduce a new definition of logical consequence in the context of active logic, as well as a new definition of soundness such that, when reasoning with consistent premises, all classically sound rules remain sound in our new sense. However, not everything that is classically sound remains sound in our sense, for by classical definitions, all rules with contradictory premises are vacuously sound, whereas in active logic not everything follows from a contradiction.     

The expressiveness of locally stratified programs

This paper completes an investigation of the logical expressibility of finite, locally stratified, general logic programs. We show that every hyperarithmetic set can be defined by a suitably chosen locally stratified logic program (as a set of values of a predicate over its perfect model). This is an optimal result, since the perfect model of a locally stratified program is itself an implicitly definable hyperarithmetic set (under a recursive coding of the Herbrand base); hence, to obtain all hyperarithmetic sets requires something new, in this case selecting one predicate from the model. We find that the expressive power of programs does not increase when one considers the programs which have a unique stable model or a total well-founded model. This shows that all these classes of structures (perfect models of logically stratified logic programs, well-founded models which turn out to be total, and stable models of programs possessing a unique stable model) are all closely connected with Kleene's hyperarithmetical hierarchy. Thus, for general logic programming, negation with respect to two-valued logic is related to the hyperarithmetic hierarchy in the same way as Horn logic is to the class of recursively enumerable sets. In particular, a set is definable in the well-founded semantics by a programP whose well-founded partial model is total iff it is hyperarithmetic.Research partially supported by the U.S. Army Research Office through the Mathematical Sciences Institute of Cornell University.Research partially supported by NSF Grant IRI-9012902 and partially supported by the U.S. Army Research Office through the Mathematical Sciences Institute of Cornell University.Research partially supported by NSF Grant IRI-8905166 and partially supported by the U.S. Army Research Office through the Mathematical Sciences Institute of Cornell University.