In this paper, we present a framework for the semantics and the computation of aggregates in the context of logic programming. In our study, an aggregate can be an arbitrary interpreted second order predicate or function. We define extensions of the Kripke-Kleene, the well-founded and the stable semantics for aggregate programs. The semantics is based on the concept of a three-valued immediate consequence operator of an aggregate program. Such an operator approximates the standard two-valued immediate consequence operator of the program, and induces a unique Kripke-Kleene model, a unique well-founded model and a collection of stable models. We study different ways of defining such operators and thus obtain a framework of semantics, offering different trade-offs between precision and tractability. In particular, we investigate conditions on the operator that guarantee that the computation of the three types of semantics remains on the same level as for logic programs without aggregates. Other results show that, in practice, even efficient three-valued immediate consequence operators which are very low in the precision hierarchy, still provide optimal precision.
Another proposal for a stable semantics of disjunctive logic programs extended with aggregates was given in @cite_27 . In the sequel we investigate in more detail the relationship with this semantics to the family of @math -stable semantics defined earlier. First, we recall the definitions of the stable semantics of @cite_27 .
@cite_27
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We introduce a family of partial stable model semantics for logic programs with arbitrary aggregate relations. The semantics are parametrized by the interpretation of aggregate relations in three-valued logic. Any semantics in this family satisfies two important properties: (i) it extends the partial stable semantics for normal logic programs and (ii) total stable models are always minimal. We also give a specific instance of the semantics and show that it has several attractive features. In this paper, we propose an extension of the well-founded and stable model semantics for logic programs with aggregates. Our approach uses Approximation Theory, a fixpoint theory of stable and well-founded fixpoints of non-monotone operators in a complete lattice. We define the syntax of logic programs with aggregates and define the immediate consequence operator of such programs. We investigate the well-founded and stable semantics generated by Approximation Theory. We show that our approach extends logic programs with stratified aggregation and that it correctly deals with well-known benchmark problems such as the shortest path program and the company control problem. The addition of aggregates has been one of the most relevant enhancements to the language of answer set programming (ASP). They strengthen the modelling power of ASP in terms of natural and concise problem representations. Previous semantic definitions typically agree in the case of non-recursive aggregates, but the picture is less clear for aggregates involved in recursion. Some proposals explicitly avoid recursive aggregates, most others differ, and many of them do not satisfy desirable criteria, such as minimality or coincidence with answer sets in the aggregate-free case. In this paper we define a semantics for programs with arbitrary aggregates (including monotone, antimonotone, and nonmonotone aggregates) in the full ASP language allowing also for disjunction in the head (disjunctive logic programming - DLP). This semantics is a genuine generalization of the answer set semantics for DLP, it is defined by a natural variant of the Gelfond-Lifschitz transformation, and treats aggregate and non-aggregate literals in a uniform way. This novel transformation is interesting per se also in the aggregate-free case, since it is simpler than the original transformation and does not need to differentiate between positive and negative literals. We prove that our semantics guarantees the minimality (and therefore the incomparability) of answer sets, and we demonstrate that it coincides with the standard answer set semantics on aggregate-free programs. Moreover, we carry out an in-depth study of the computational complexity of the language. The analysis pays particular attention to the impact of syntactical restrictions on programs in the form of limited use of aggregates, disjunction, and negation. While the addition of aggregates does not affect the complexity of the full DLP language, it turns out that their presence does increase the complexity of normal (i.e., non-disjunctive) ASP programs up to the second level of the polynomial hierarchy. However, we show that there are large classes of aggregates the addition of which does not cause any complexity gap even for normal programs, including the fragment allowing for arbitrary monotone, arbitrary antimonotone, and stratified (i.e., non-recursive) nonmonotone aggregates. The analysis provides some useful indications on the possibility to implement aggregates in existing reasoning engines. Aggregates are functions that take sets as arguments. Examples are the function that maps a set to the number of its elements or the function which maps a set to its minimal element. Aggregates are frequently used in relational databases and have many applications in combinatorial search problems and knowledge representation. Aggregates are of particular importance for several extensions of logic programming which are used for declarative programming like Answer Set Programming, Abductive Logic Programming, and the logic of inductive definitions (ID-Logic). Aggregate atoms not only allow a broader class of problems to be represented in a natural way but also allow a more compact representation of problems which often leads to faster solving times. Extensions of specific semantics of logic programs with, in many cases, specific aggregate relations have been proposed before. The main contributions of this thesis are: (i) we extend all major semantics of logic programs: the least model semantics of definite logic programs, the standard model semantics of stratified programs, the Clark completion semantics, the well-founded semantics, the stable models semantics, and the three-valued stable semantics; (ii) our framework admits arbitrary aggregate relations in the bodies of rules. We follow a denotational approach in which a semantics is defined as a (set of) fixpoint(s) of an operator associated with a program. The main tool of this work is Approximation Theory. This is an algebraic theory which defines different types of fixpoints of an approximating operator associated with a logic program. All major semantics of a logic program correspond to specific types of fixpoints of an approximating operator introduced by Fitting. We study different approximating operators for aggregate programs and investigate the precision and complexity of the semantics generated by them. We study in detail one specific operator which extends the Fitting operator and whose semantics extends the three-valued stable semantics of logic programs without aggregates. We look at algorithms, complexity, transformations of aggregate atoms and programs, and an implementation in XSB Prolog.