Nested expressions in logic programs

We extend the answer set semantics to a class of logic programs with nested expressions permitted in the bodies and heads of rules. These expressions are formed from literals using negation as failure, conjunction (,) and disjunction (;) that can be nested arbitrarily. Conditional expressions are introduced as abbreviations. The study of equivalent transformations of programs with nested expressions shows that any such program is equivalent to a set of disjunctive rules, possibly with negation as failure in the heads. The generalized answer set semantics is related to the Lloyd–Topor generalization of Clark’s completion and to the logic of minimal belief and negation as failure. This revised version was published online in June 2006 with corrections to the Cover Date.     

Logic Programs with Ordered Disjunction

Logic programs with ordered disjunction (LPODs) contain a new connective which allows representing alternative, ranked options for problem solutions in the heads of rules: A × B intuitively means that if possible A , but if A is not possible, then at least B . The semantics of logic programs with ordered disjunction is based on a preference relation on answer sets. We show how LPODs can be implemented using answer set solvers for normal programs. The implementation is based on a generator, which produces candidate answer sets and a tester which checks whether a given candidate is maximally preferred and produces a better candidate if it is not. We also discuss the complexity of reasoning tasks based on LPODs and possible applications.     

The Least Fixpoint Transformation for Disjunctive Logic Programs

The paradigm of disjunctive logic programming(DLP)enhances greatly the expressive power of normal logic programming(NLP)and many(declarative)semantics have been defined for DLP to cope with various problems of knowledge representation in artificial intelligence.However,the expressive ability of the semantics and the soundness of program transformations for DLP have been rarely explored.This paper defines an immediate consequence operatro T^GP for each disjunctive program and shows that T^GP has the least and computable fixpoint Lft(P),Lft is,in fact,a program transformation for DLP,which transforms all disjunctive programs into negative programs.It is shown that Lft preserves many key semantics,including the disjunctive stable models,well-founded model,disjunctive argunent semantics DAS,three-valued models,ect.Thic means that every disjunctive program P has a unique canonical form Lft(P)with respect to these semantics.As a result,the work in this paper provides a unifying framework for studying the expressive ability of various semantics for DLP On the other hand,the computing of the above semantics for negative programs is ust a trivial task,therefore,Lft(P)is also an optimization method for DLP.Another application of Lft is to derive some interesting semantic results for DLP.     

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.     

Active Integrity Constraints for Database Consistency Maintenance

This paper introduces active integrity constraints (AICs), an extension of integrity constraints for consistent database maintenance. An active integrity constraint is a special constraint whose body contains a conjunction of literals which must be false and whose head contains a disjunction of update actions representing actions (insertions and deletions of tuples) to be performed if the constraint is not satisfied (that is its body is true). The AICs work in a domino-like manner as the satisfaction of one AIC may trigger the violation and therefore the activation of another one. The paper also introduces founded repairs, which are minimal sets of update actions that make the database consistent, and are specified and “supported” by active integrity constraints. The paper presents: 1) a formal declarative semantics allowing the computation of founded repairs and 2) a characterization of this semantics obtained by rewriting active integrity constraints into disjunctive logic rules, so that founded repairs can be derived from the answer sets of the derived logic program. Finally, the paper studies the computational complexity of computing founded repairs.     

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.     

Weight constraint programs with evaluable functions

In the current practice of Answer Set Programming (ASP), evaluable functions are represented as special kinds of relations. This often makes the resulting program unnecessarily large when instantiated over a large domain. The extra constraints needed to enforce the relation as a function also make the logic program less transparent. In this paper, we consider adding evaluable functions to answer set logic programs. The class of logic programs that we consider here is that of weight constraint programs, which are widely used in ASP. We propose an answer set semantics to these extended weight constraint programs and define loop completion to characterize the semantics. Computationally, we provide a translation from loop completions of these programs to instances of the Constraint Satisfaction Problem (CSP) and use the off-the-shelf CSP solvers to compute the answer sets of these programs. A main advantage of this approach is that global constraints implemented in such CSP solvers become available to ASP. The approach also provides a new encoding for CSP problems in the style of weight constraint programs. We have implemented a prototype system based on these results, and our experiments show that this prototype system competes well with the state-of-the-art ASP solvers. In addition, we illustrate the utilities of global constraints in the ASP context.     

The semantics of answer literals

We describe a semantics for answer literals that is not tied to the specific detaisl of the resolution proof procedure. We also describe a number of applications of answer literals to mathematical theorem proving.Author supported by NSF Grant CCR-9503445.     

Integrating answer set programming and constraint logic programming

We introduce a knowledge representation language ${\cal AC(C)}$ extending the syntax and semantics of ASP and CR-Prolog, give some examples of its use, and present an algorithm, $\mathcal{AC}\!solver$ , for computing answer sets of ${\cal AC(C)}$ programs. The algorithm does not require full grounding of a program and combines “classical” ASP solving methods with constraint logic programming techniques and CR-Prolog based abduction. The ${\cal AC(C)}$ based approach often allows to solve problems which are impossible to solve by more traditional ASP solving techniques. We believe that further investigation of the language and development of more efficient and reliable solvers for its programs can help to substantially expand the domain of applicability of the answer set programming paradigm.     

On look-ahead heuristics in disjunctive logic programming

Disjunctive logic programming (DLP), also called answer set programming (ASP), is a convenient programming paradigm which allows for solving problems in a simple and highly declarative way. The language of DLP is very expressive and able to represent even problems of high complexity (every problem in the complexity class ${{\Sigma}_{2}^{P}} = {\rm NP}^{{\rm NP}}$ ). During the last decade, efficient systems supporting DLP have become available. Virtually all of these systems internally rely on variants of the Davis–Putnam procedure (for deciding propositional satisfiability [SAT]), combined with a suitable model checker. The heuristic for the selection of the branching literal (i.e., the criterion determining the literal to be assumed true at a given stage of the computation) dramatically affects the performance of a DLP system. While heuristics for SAT have received a fair deal of research, only little work on heuristics for DLP has been done so far. In this paper, we design, implement, optimize, and experiment with a number of heuristics for DLP. We focus on different look-ahead heuristics, also called “dynamic heuristics” (the DLP equivalent of unit propagation [UP] heuristics for SAT). These are branching rules where the heuristic value of a literal Q depends on the result of taking Q true and computing its consequences. We motivate and formally define a number of look-ahead heuristics for DLP programs. Furthermore, since look-ahead heuristics are computationally expensive, we design two techniques for optimizing the burden of their computation. We implement all the proposed heuristics and optimization techniques in DLV—the state-of-the-art implementation of disjunctive logic programming, and we carry out experiments, thoroughly comparing the heuristics and optimization techniques on a large number of instances of well-known benchmark problems. The results of these experiments are very interesting, showing that the proposed techniques significantly improve the performance of the DLV system.     

一种基于事件的Web服务组合方法

为获得一种既易于实现又能满足用户多样化需求的服务组合的有效途径,提出一种基于事件的服务组合方法.首先定义了一种基于ECA(event-condition-action)规则的语言——简单服务事件语言.在这种语言基础上,通过模块化方法构造的用于描述组合服务的组合方案,不但解决了采用AI规划(artificial intelligent planning)时服务组合域表示困难的问题,而且解决了采用UML(unified modeling language)等技术时描述能力不足的问题.随后,为有效地表示组合方案,完成了它的语义定义以及answer set程序编码工作.最后利用answer set编程(answer set programming)技术实现了对组合轨迹的表示.     

Reducts of propositional theories,satisfiability relations,and generalizations of semantics of logic programs

Over the years, the stable-model semantics has gained a position of the correct (two-valued) interpretation of default negation in programs. However, for programs with aggregates (constraints), the stable-model semantics, in its broadly accepted generalization stemming from the work by Pearce, Ferraris and Lifschitz, has a competitor: the semantics proposed by Faber, Leone and Pfeifer, which seems to be essentially different. Our goal is to explain the relationship between the two semantics. Pearce, Ferraris and Lifschitz's extension of the stable-model semantics is best viewed in the setting of arbitrary propositional theories. We propose here an extension of the Faber–Leone–Pfeifer semantics, or FLP semantics, for short, to the full propositional language, which reveals both common threads and differences between the FLP and stable-model semantics. We use our characterizations of FLP-stable models to derive corresponding results on strong equivalence and on normal forms of theories under the FLP semantics. We apply a similar approach to define supported models for arbitrary propositional theories, and to study their properties.     

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.     

Compositional operational semantics for Prolog programs

To relate operational semantics of logic programs to its declarative semantics, we have to rely on SLD-trees. But this form of operational semantics does not make it easy to relate execution behaviour to program structure. The reason is that the traversal of SLD-trees decomposes in a way that is different from the decomposition (which is by disjunction and conjunction) of programs. We propose SLD-contours, a variant of SLD-trees, that are, like programs, built up out of simpler components by means of disjunction and conjunction. The traversal of SLD-trees carries over to the traversal of SLD-contours in such a way that the trace events of the Box Model (Try, Succeed, Retry, Fail) are reproduced in the same way as during the execution of the program. ThusSLD-contours relate the trace more closely to the program than SLD-trees. SLD-contours specify the trace more completely than the Box Model does.     

Complexity of computing with extended propositional logic programs

In this paper we introduce the notion of anF-program, whereF is a collection of formulas. We then study the complexity of computing withF-programs.F-programs can be regarded as a generalization of standard logic programs. Clauses (or rules) ofF-programs are built of formulas fromF. In particular, formulas other than atoms are allowed as building blocks ofF-program rules. Typical examples ofF are the set of all atoms (in which case the class of ordinary logic programs is obtained), the set of all literals (in this case, we get the class of logic programs with classical negation [9]), the set of all Horn clauses, the set of all clauses, the set of all clauses with at most two literals, the set of all clauses with at least three literals, etc. The notions of minimal and stable models [16, 1, 7] of a logic program have natural generalizations to the case ofF-programs. The resulting notions are called in this paperminimal andstable answer sets. We study the complexity of reasoning involving these notions. In particular, we establish the complexity of determining the existence of a stable answer set, and the complexity of determining the membership of a formula in some (or all) stable answer sets. We study the complexity of the existence of minimal answer sets, and that of determining the membership of a formula in all minimal answer sets. We also list several open problems.This work was partially supported by National Science Foundation under grant IRI-9012902.This work was partially supported by National Science Foundation under grant CCR-9110721.     

A procedural semantics for stratified database updates

This paper investigates efficient evaluation of database updates and presents a procedural semantics for stratified update programs that extend stratified logic programs with bulk updates and hypothetical reasoning. Bulk rules with universal quantification in the body allow an arbitrary update to be applied simultaneously for every answer of an arbitrary query. Hypothetical reasoning is supported by testing the success or failure of an update. The procedural semantics offers efficient goal-oriented tabled evaluation of database updates. It guarantees termination for function-free stratified update programs and avoids repeated computation of identical subgoals. This revised version was published online in June 2006 with corrections to the Cover Date.     

Programming with logical queries, bulk updates, and hypotheticalreasoning

The paper presents a language of update programs that integrates logical queries, bulk updates and hypothetical reasoning in a seamless manner. There is no syntactic or semantic distinction between queries and updates. Update programs extend logic programs with negation in both syntax and semantics. They allow bulk updates in which an arbitrary update is applied simultaneously for all answers of an arbitrary query. Hypothetical reasoning is naturally supported by testing the success or failure of an update. We describe an alternating fixpoint semantics of update programs and show that it can express all nondeterministic database transformations     

一个带破圈启发方法的回答集编程系统

回答集编程(answer set programming,ASP)是一种回答集语义下的逻辑编程范例,可应用于非单调推理,叙述式问题求解等领域.本文为ASP提出并实现了一种破圈启发方法与一种基部限制式前向搜索过程,所得到的系统称为LPS.实验结果显示,相对于其他经典的ASP系统,LPS能够有效地解决处于相变难区域中的逻辑程序,通常这些程序被认为是计算困难的.除此以外,通过使用被称为动态变元过滤(dynamic variable filtering,DVF)的技术,LPS可以在计算过程中极大地缩小搜索树的尺寸.