On Systematic Construction of Correct Logic Programs

Logic programs with aggregates and description logic programs (dl-programs) are two recent extensions to logic programming. In this paper, we study the relationships between these two classes of logic programs, under the well-founded semantics. The main result is that, under a satisfaction-preserving mapping from dl-atoms to aggregates, the well-founded semantics of dl-programs by Eiter et al., coincides with the well-founded semantics of aggregate programs, defined by Pelov et al. as the least fixpoint of a 3-valued immediate consequence operator under the ultimate approximating aggregate. This result enables an alternative definition of the same well-founded semantics for aggregate programs, in terms of the first principle of unfounded sets. Furthermore, the result can be applied, in a uniform manner, to define the well-founded semantics for dl-programs with aggregates, which agrees with the existing semantics when either dl-atoms or aggregates are absent.

Relating defeasible and normal logic programming through transformation properties

M. Bezem defined an extensional semantics for positive higher-order logic programs. Recently, it was demonstrated by Rondogiannis and Symeonidou that Bezem's technique can be extended to higher-order logic programs with negation, retaining its extensional properties, provided that it is interpreted under a logic with an infinite number of truth values. Rondogiannis and Symeonidou also demonstrated that Bezem's technique, when extended under the stable model semantics, does not in general lead to extensional stable models. In this paper, we consider the problem of extending Bezem's technique under the well-founded semantics. We demonstrate that the well-founded extensionfailsto retain extensionality in the general case. On the positive side, we demonstrate that for stratified higher-order logic programs, extensionality is indeed achieved. We analyze the reasons of the failure of extensionality in the general case, arguing that a three-valued setting cannot distinguish between certain predicates that appear to have a different behaviour inside a program context, but which happen to be identical as three-valued relations.

Skepticism is one of the most important semantic intuitions in artificial intelligence. The semantics formalizing skeptical reasoning in (disjunctive) logic programming is usually named well-founded semantics. However, the issue of defining and computing the well-founded semantics for disjunctive programs and databases has proved to be far more complex and difficult than for normal logic programs. The argumentation-based semantics WFDS is among the most promising proposals that attempts to define a natural well-founded semantics for disjunctive programs. In this paper, we propose a top-down procedure for WFDS called D-SLS Resolution, which naturally extends the Global SLS-resolution and SLI-resolution. We prove that D-SLS Resolution is sound and complete with respect to WFDS. This result in turn provides a further yet more powerful argument in favor of the WFDS.KeywordsLogic ProgramLogic ProgrammingTree NodeNegation NodeDisjunctive ProgramThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Logic programs, deductive databases, and more generally non-monotonic theories, use various forms of default negation, not F , whose major distinctive feature is that not F is assumed “by default”, i.e., it is assumed in the absence of sufficient evidence to the contrary. The meaning of “sufficient evidence” depends on the specific semantics used. For example, in Reiter’s Closed World Assumption, CWA [32], not A is assumed for atomic A if A is not provable, or, equivalently, if there is a minimal model in which A is false. On the other hand, in Minker’s Generalized Closed World Assumption, GCWA [18, 16], or in McCarthy’s Circumscription, CIRC [17], not A is assumed only if A is false in all minimal models. In Clark’s Predicate Completion semantics for logic programs [7], this form of negation is called negation-as-failure because not A is derivable whenever attempts to prove A finitely fail. The more recent semantics proposed for logic programs and deductive databases, such as the stable semantics [14], well-founded semantics [34], partial stable or stationary semantics [27] and static semantics [30], propose even more sophisticated meanings for default negation, closely related to more general nonmonotonic formalisms such as Default Logic, DL [33], AutoEpistemic Logic, AEL [19], and AutoEpistemic logic of Beliefs, AEB [29]. Under all of these semantics, however, default negation “not ” significantly differs from classical negation “¬”. For example, the formula charged(x) ∧ ¬guilty(x) ⊃ acquitted(x) says that a person charged with a crime should be acquitted if he or she is actually proven to be not guilty. On the other hand, the formula charged(x) ∧ not guilty(x) ⊃ acquitted(x) says that one should be acquitted of any charges by default unless sufficient evidence of that person’s guilt is demonstrated.

A variety of proposals for forgetting in logic programs under different semantics have emerged that satisfy differing sets of properties considered desirable. Despite the achieved progress in devising approaches that capture an increasing number of these properties, the idea that the result of forgetting should preserve the meaning of the initial program for the remaining, non-forgotten, atoms, has not yet been captured. In particular, the existing proposals may not preserve dependency relations between such atoms that are given by the structure of the program. In logic programs, these relations are captured by strong equivalence, but, preserving strong equivalence of two different programs while forgetting does not suffice. Rather, strong equivalence relativized to the remaining atoms should be preserved between the original program and the one that results from forgetting. In this paper, we overcome this deficiency by formalizing the property that captures this maintenance of relations while forgetting, and at the same time a general semantic definition for such a forgetting for arbitrary logic programs. Then, we study forgetting for normal programs under the well-founded semantics, and for programs with double negation under the answer set semantics. In both cases, we focus on efficient syntax-based algorithms that only manipulate the rules in which changes are effectively necessary.

The main goal of this paper is to establish a nonmonotonic epistemic logic eB with two modalities — provability and belief — capable of expressing and comparing a variety of known semantics for extended logic programs, and clarify their meaning. In particular we present here, for the first time, embeddings into epistemic logic of logic programs extended with a second kind of negation under the well-founded semantics, and contrast them to the recent embeddings into autoepistemic logics of such programs under stable models based semantics.

This is the second in a series of workshops that are bringing together researchers from the theoretical end of both the logic programming and artificial intelligence communities to discuss their mutual interests. This workshop emphasizes the relationship between logic programming and non-monotonic reasoning.Luis' Moniz Pereira is Professor in the Department of Computer Science at the Universidade Nova Lisboa, Portugal. Anil Nerode is Professor and Director of the Mathematical Sciences Institute at Cornell University.Topics include: Stable Semantics. Autoepistemic Logic. Abduction. Implementation Issues. Well-founded Semantics. Truth Maintenance. Probabilistic Theories. Applications. Default Logic. Diagnosis. Complexity and Theory. Handling Inconsistency.

We discuss proving correctness and completeness of definite clause logic programs. We propose a method for proving completeness, while for proving correctness we employ a method that should be well known but is often neglected. Also, we show how to prove completeness and correctness in the presence of SLD-tree pruning, and point out that approximate specifications simplify specifications and proofs.We compare the proof methods to declarative diagnosis (algorithmic debugging), showing that approximate specifications eliminate a major drawback of the latter. We argue that our proof methods reflect natural declarative thinking about programs, and that they can be used, formally or informally, in everyday programming.

Hybrid MKNF knowledge bases are one of the most prominent tightly integrated combinations of open-world ontology languages with closed-world (nonmonotonic) rule paradigms. Based on the logic of minimal knowledge and negation as failure (MKNF), the definition of Hybrid MKNF is parametric on the description logic (DL) underlying the ontology language, in the sense that nonmonotonic rules can extend any decidable DL language. Two related semantics have been defined for Hybrid MKNF: one that is based on the Stable Model Semantics for logic programs and one on the Well-Founded Semantics (WFS). Under WFS, the definition of Hybrid MKNF relies on a bottom-up computation that has polynomial data complexity whenever the DL language is tractable. Here we define a general query-driven procedure for Hybrid MKNF that is sound with respect to the stable model-based semantics, and sound and complete with respect to its WFS variant. This procedure is able to answer a slightly restricted form of conjunctive queries, and is based on tabled rule evaluation extended with an external oracle that captures reasoning within the ontology. Such an (abstract) oracle receives as input a query along with knowledge already derived, and replies with a (possibly empty) set of atoms, defined in the rules, whose truth would suffice to prove the initial query. With appropriate assumptions on the complexity of the abstract oracle, the general procedure maintains the data complexity of the WFS for Hybrid MKNF knowledge bases. To illustrate this approach, we provide a concrete oracle for EL + , a fragment of the lightweight DL EL ++ . Such an oracle has practical use, as EL ++ is the language underlying OWL 2 EL, which is part of the W3C recommendations for the Semantic Web, and is tractable for reasoning tasks such as subsumption. We show that query-driven Hybrid MKNF preserves polynomial data complexity when using the EL + oracle and WFS.

Logic programming has developed as a rich field, built over a logical substratum whose main constituent is a nonclassical form of negation, sometimes coexisting with classical negation. The field has seen the advent of a number of alternative semantics, with Kripke–Kleene semantics, the well-founded semantics, the stable model semantics, and the answer-set semantics standing out as the most successful. We show that all aforementioned semantics are particular cases of a generic semantics, in a framework where classical negation is the unique form of negation and where the literals in the bodies of the rules can be ‘marked’ to indicate that they can be the targets of hypotheses. A particular semantics then amounts to choosing a particular marking scheme and choosing a particular set of hypotheses. When a literal belongs to the chosen set of hypotheses, all marked occurrences of that literal in the body of a rule are assumed to be true, whereas the occurrences of that literal that have not been marked in the body of the rule are to be derived in order to contribute to the firing of the rule. Hence the notion of hypothetical reasoning that is presented in this framework is not based on making global assumptions, but more subtly on making local, contextual assumptions, taking effect as indicated by the chosen marking scheme on the basis of the chosen set of hypotheses. Our approach offers a unified view on the various semantics proposed in logic programming, classical in that only classical negation is used, and links the semantics of logic programs to mechanisms that endow rule-based systems with the power to harness hypothetical reasoning.

On the equivalence of the static and disjunctive well-founded semantics and its computation

The well-founded semantics is one of the most widely studied and used semantics of logic programs with negation. In the case of finite propositional programs, it can be computed in polynomial time, more specifically, in O([mid ]At(P)[mid ] × size(P)) steps, where size(P) denotes the total number of occurrences of atoms in a logic program P. This bound is achieved by an algorithm introduced by Van Gelder and known as the alternating-fixpoint algorithm. Improving on the alternating-fixpoint algorithm turned out to be difficult. In this paper we study extensions and modifications of the alternating-fixpoint approach. We then restrict our attention to the class of programs whose rules have no more than one positive occurrence of an atom in their bodies. For programs in that class we propose a new implementation of the alternating-fixpoint method in which false atoms are computed in a top-down fashion. We show that our algorithm is faster than other known algorithms and that for a wide class of programs it is linear and so, asymptotically optimal.

We address methods of speeding up the calculation of the well-founded semantics for normal propositional logic programs. We first consider two algorithms already reported in the literature and show that these, plus a variation upon them, have much improved worst-case behavior for special cases of input. Then we propose a general algorithm to speed up the calculation for logic programs with at most two positive subgoals per clause, intended to improve the worst case performance of the computation. For a logic program P in atoms A1, the speed up over the straight Van Gelder alternating fixed point algorithm (assuming worst-case behavior for both algorithms) is approximately (¦P¦/¦A¦)(1/3). For ¦P¦≥¦A¦4, the algorithm runs in time linear in ¦P¦.

Knowledge representation and inference in AI have been traditionally divided between logic-based and statistical approaches. During the past decade, the rapidly developing area of Statistical Relational Learning aims to combine the two frameworks for representation and inference. In many cases, these works include probabilistic reasoning within Logic Programming frameworks. These attempts are restricted in the sense that they use only two-valued negation-as-failure semantics. However, well-founded semantics is a widely accepted three-valued-logic negation semantics scheme, which is implemented in certain Logic Programming frameworks. In this paper we introduce probabilistic inference under the well-founded semantics scheme in a single Probabilistic Logic Programming framework, where the uncertainty can be described using both statistical information (probabilities) and a third logic value.