Comparison of Semantics of Disjunctive Logic Programs Based on Model-Equivalent Reduction

In this paper, it is shown that stable model semantics, perfect model semantics, and partial stable model semantics of disjunctive logic programs have the same expressive power with respect to the polynomial-time model-equivalent reduction. That is, taking perfect model semantics and stable model semantic as an example, any logic program P can be transformed in polynomial time to another logic program P＇ such that perfect models （resp. stable models） of P i-i correspond to stable models （resp. perfect models） of P＇, and the correspondence can be computed also in polynomial time. However, the minimal model semantics has weaker expressiveness than other mentioned semantics, otherwise, the polynomial hierarchy would collapse to NP.     

Parallel complexity of logical query programs

We consider the parallel time complexity of logic programs without function symbols, called logical query programs, or Datalog programs. We give a PRAM algorithm for computing the minimum model of a logical query program, and show that for programs with the “polynomial fringe property,” this algorithm runs in time that is logarithmic in the input size, assuming that concurrent writes are allowed if they are consistent. As a result, the “linear” and “piecewise linear” classes of logic programs are inN C. Then we examine several nonlinear classes in which the program has a single recursive rule that is an “elementary chain.” We show that certain nonlinear programs are related to GSM mappings of a balanced parentheses language, and that this relationship implies the “polynomial fringe property;” hence such programs are inN C Finally, we describe an approach for demonstrating that certain logical query programs are log space complete forP, and apply it to both elementary single rule programs and nonelementary programs.     

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.     

Extremal problems in logic programming and stable model computation

We study the following problem: given a class of logic programs ¢, determine the maximum number of stable models of a program from ©. We establish the maximum for the class of all logic programs with at most n clauses, and for the class of all logic programs of size at most n. We also characterize the programs for which the maxima are attained. We obtained similar results for the class of all disjunctive logic programs with at most n clauses, each of length at most m, and for the class of all disjunctive logic programs of size at most n. Our results on logic programs have direct implication for the design of algorithms to compute stable models. Several such algorithms, similar in spirit to the Davis-Putnam procedure, are described in the paper. Our results imply that there is an algorithm that finds all stable models of a program with n clauses after considering the search space of size O(3^n/3) in the worst case. Our results also provide some insights into the question of representability of families of sets as families of stable models of logic programs.     

Disjunctive LP+integrity constraints= stable model semantics

We show that stable models of logic programs may be viewed as minimal models of programs that satisfy certain additional constraints. To do so, we transform the normal programs into disjunctive logic programs and sets of integrity constraints. We show that the stable models of the normal program coincide with the minimal models of the disjunctive program thatsatisfy the integrity constraints. As a consequence, the stable model semantics can be characterized using theextended generalized closed world assumption for disjunctive logic programs. Using this result, we develop a bottomup algorithm for function-free logic programs to find all stable models of a normal program by computing the perfect models of a disjunctive stratified logic program and checking them for consistency with the integrity constraints. The integrity constraints provide a rationale as to why some normal logic programs have no stable models.     

An empirical study of the 4‐valued Kripke–Kleene and 4‐valued well‐founded semantics in random propositional logic programs

We empirically investigated the difficulty of finding stable models for logic programs using backtracking, by trying to identify what makes random instances easy or hard. Additionally, we empirically investigated the effectiveness of the 4‐valued Kripke–Kleene semantics (4KK) and the 4‐valued well‐founded semantics (4WF) in the Niemelä and Simons’ backtracking algorithm, smodels, for finding stable models. We studied the behavior of 4KK and 4WF in a parameterized distribution of random propositional logic programs of fixed rule‐length k. In all of our experiments, 4KK and 4WF (both modified to extend an input partial truth assignment) were computed with respect to a fixed percentage of proposition letters (randomly chosen) initially assigned TRUE and a fixed percentage (randomly chosen) initially assigned FALSE. There exists a region, R, in the parameter space of our distribution where smodels required a large number of recursive calls to determine if programs generated in this region have any stable models. Hence, the “hardest” programs for smodels to determine if a stable model exists lie in R. Additionally, there exists a subregion of R where smodels made significantly fewer recursive calls when using 4WF as a pruning technique than when using 4KK. To gain a deeper insight into the causes for the “hardness” of programs in R and the differences between 4WF and 4KK as pruning techniques in smodels, we examined more closely the behavior of 4KK and 4WF. There exists a region in which a very small percentage of inconsistent models were produced by both 4KK and 4WF, thereby providing very little information useful for smodels to immediately backtrack. This region roughly corresponded to the above region where smodels required a large number of recursive calls. Also, there exists a region in which both 4KK and 4WF produced a high percentage of inconsistent models, thereby providing information useful for smodels to immediately backtrack.     

Reasoning with infinite stable models

This paper illustrates extensively the theoretical properties, the implementation issues, and the programming style underlying finitary programs. They are a class of normal logic programs whose consequences under the stable model semantics can be effectively computed, despite the fact that finitary programs admit function symbols (hence infinite domains) and recursion. From a theoretical point of view, finitary programs are interesting because they enjoy properties that are extremely unusual for a nonmonotonic formalism, such as compactness. From the application point of view, the theory of finitary programs shows how the existing technology for answer set programming can be extended from problem solving below the second level of the polynomial hierarchy to all semidecidable problems. Moreover, finitary programs allow a more natural encoding of recursive data structures and may increase the performance of credulous reasoners.     

Logic Programs,Compatibility and Forward Chaining Construction

Logic programming under the stable model semantics is proposed as a non-monotonic language for knowledge representation and reasoning in artificial intelligence. In this paper, we explore and extend the notion of compatibility and the Λ operator, which were first proposed by Zhang to characterize default theories. First, we present a new characterization of stable models of a logic program and show that an extended notion of compatibility can characterize stable submodels. We further propose the notion of weak auto-compatibility which characterizes the Normal Forward Chaining Construction proposed by Marek, Nerode and Remmel. Previously, this construction was only known to construct the stable models of FC-normal logic programs, which turn out to be a proper subclass of weakly auto-compatible logic programs. We investigate the properties and complexity issues for weakly auto-compatible logic programs and compare them with some subclasses of logic programs.     

A new fixpoint semantics for general logic programs compared with the well-founded and the stable model semantics

We study a new fixpoint semantics for logic programs with negation. Our construction is intermediate between Van Gelder’s well-founded model and Gelfond and Lifschitz’s stable model semantics. We show first that the stable models of a logic programP are exactly the well-supported models ofP, i.e. the supported models with loop-free finite justifications. Then we associate to any logic programP a non-monotonic operator over the semilattice of justified interpretations, and we define in an abstract form its ordinal powers. We show that the fixpoints of this operator are the stable models ofP, and that its ordinal powers after some ordinala are extensions of the well-founded partial model ofP. In particular ifP has a well-founded model then that canonical model is also an ordinal power and the unique fixpoint of our operator. We show with examples of logic programs which have a unique stable model but no well-founded model that the converse is false. We relate also our work to Doyle’s truth maintenance system and some implementations of rule-based expert systems.     

Using Computer Algebra techniques for the specification,verification and synthesis of recursive programs

We describe an innovative method for proving total correctness of tail recursive programs having a specific structure, namely programs in which an auxiliary tail recursive function is driven by a main nonrecursive function, and only the specification of the main function is provided. The specification of the auxiliary function is obtained almost fully automatically by solving coupled linear recursive sequences with constant coefficients. The process is carried out by means of CA (Computer Algebra) and AC (Algorithmic Combinatorics) and is implemented in the Theorema system (using Mathematica). We demonstrate this method on an example involving polynomial expressions. Furthermore, we develop a method for synthesis of recursive programs for computing polynomial expressions of a fixed degree by means of “cheap” operations, e.g., additions, subtractions and multiplications. For a given polynomial expression, we define its recursive program in a schemewise manner. The correctness of the synthesized programs follows from the general correctness of the synthesis method, which is proven once for all, using the verification method presented in the first part of this paper.     

Propositional semantics for disjunctive logic programs

In this paper we study the properties of the class of head-cycle-free extended disjunctive logic programs (HEDLPs), which includes, as a special case, all nondisjunctive extended logic programs. We show that any propositional HEDLP can be mapped in polynomial time into a propositional theory such that each model of the latter corresponds to an answer set, as defined by stable model semantics, of the former. Using this mapping, we show that many queries over HEDLPs can be determined by solving propositional satisfiability problems. Our mapping has several important implications: It establishes the NP-completeness of this class of disjunctive logic programs; it allows existing algorithms and tractable subsets for the satisfiability problem to be used in logic programming; it facilitates evaluation of the expressive power of disjunctive logic programs; and it leads to the discovery of useful similarities between stable model semantics and Clark's predicate completion.     

Stable models and difference logic

The paper studies the relationship between logic programs with the stable model semantics and difference logic recently considered in the Satisfiability Modulo Theories framework. Characterizations of stable models in terms of level rankings are developed building on simple linear integer constraints allowed in difference logic. Based on the characterizations translations are devised which map normal programs to difference logic formulas capturing stable models of a program as satisfying valuations of the resulting formula. The translations make it possible to use a solver for difference logic to compute stable models of logic programs.     

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.

     

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.     

Possibilistic uncertainty handling for answer set programming

In this work, we introduce a new framework able to deal with a reasoning that is at the same time non monotonic and uncertain. In order to take into account a certainty level associated to each piece of knowledge, we use possibility theory to extend the non monotonic semantics of stable models for logic programs with default negation. By means of a possibility distribution we define a clear semantics of such programs by introducing what is a possibilistic stable model. We also propose a syntactic process based on a fix-point operator to compute these particular models representing the deductions of the program and their certainty. Then, we show how this introduction of a certainty level on each rule of a program can be used in order to restore its consistency in case of the program has no model at all. Furthermore, we explain how we can compute possibilistic stable models by using available softwares for Answer Set Programming and we describe the main lines of the system that we have developed to achieve this goal.     

Logic programming with solution preferences

Preference logic programming (PLP) is an extension of logic programming for declaratively specifying problems requiring optimization or comparison and selection among alternative solutions to a query. PLP essentially separates the programming of a problem itself from the criteria specification of its solution selection. In this paper we present a declarative method for specifying preference logic programs. The method introduces a precise formalization for the syntax and semantics of PLP. The syntax of a preference logic program contains two disjoint sets of definite clauses, separating a core program specifying a general computational problem from its preference rules for optimization; the semantics of PLP is given based on the Herbrand model and fixed point theory, where how preferences affects the least Herbrand model of a logic program is interpreted as a sequence of meta-level mapping operations. In addition, we present an operational semantics based on a new resolution strategy and a memoized recursive algorithm for computing strictly stratified logic programs with well-formed preferences, and further show that the operational semantics of such a preference logic program is consistent to its declarative semantics.     

Linearizing some recursive logic programs

We give a sufficient condition under which the least fixpoint of the equation X=a+f(X)X equals the least fixpoint of the equation X=a+f(a)X. We then apply that condition to recursive logic programs containing chain rules: we translate it into a sufficient condition under which a recursive logic program containing n⩾2 recursive calls in the bodies of the rules is equivalent to a linear program containing at most one recursive call in the bodies of the rules. We conclude with a discussion comparing our condition with the other approaches to linearization studied in the literature     

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.