This paper surveys various complexity results on different forms of logic programming. The main focus is on decidable forms of logic programming, in particular, propositional logic programming and datalog, but we also mention general logic programming with function symbols. Next to classical results on plain logic programming (pure Horn clause programs), more recent results on various important extensions of logic programming are surveyed. These include logic programming with different forms of negation, disjunctive logic programming, logic programming with equality, and constraint logic programming. The complexity of the unification problem is also addressed.

Disjunctive Logic Programming (DLP) is an advanced formalism for knowledge representation and reasoning, which is very expressive in a precise mathematical sense: it allows to express every property of finite structures that is decidable in the complexity class ΣP 2 (NPNP). Thus, under widely believed assumptions, DLP is strictly more expressive than normal (disjunction-free) logic programming, whose expressiveness is limited to properties decidable in NP. Importantly, apart from enlarging the class of applications which can be encoded in the language, disjunction often allows for representing problems of lower complexity in a simpler and more natural fashion. This paper presents the DLV system, which is widely considered the state-of-the-art implementation of disjunctive logic programming, and addresses several aspects. As for problem solving, we provide a formal definition of its kernel language, function-free disjunctive logic programs (also known as disjunctive datalog), extended by weak constraints, which are a powerful tool to express optimization problems. We then illustrate the usage of DLV as a tool for knowledge representation and reasoning, describing a new declarative programming methodology which allows one to encode complex problems (up to ∆P 3-complete problems) in a declarative fashion. On the foundational side, we provide a detailed analysis of the computational complexity of the language of

The handling of user preferences is becoming an increasingly important issue in present-day information systems. Among others, preferences are used for information filtering and extraction to reduce the volume of data presented to the user. They are also used to keep track of user profiles and formulate policies to improve and automate decision making. We propose a logical framework for formulating preferences and its embedding into relational query languages. The framework is simple, and entirely neutral with respect to the properties of preferences. It makes it possible to formulate different kinds of preferences and to use preferences in querying databases. We demonstrate the usefulness of the framework through numerous examples.

The paper proposes a new knowledge representation language, called DLP , which extends disjunctive logic programming (with strong negation) by inheritance. The addition of inheritance enhances the knowledge modeling features of the language providing a natural representation of default reasoning with exceptions. A declarative model-theoretic semantics of DLP is provided, which is shown to generalize the answer set semantics of disjunctive logic programs. The knowledge modeling features of the language are illustrated by encoding classical nonmonotonic problems in DLP . The complexity of DLP is analyzed, proving that inheritance does not cause any computational overhead, as reasoning in DLP has exactly the same complexity as reasoning in disjunctive logic programming. This is conrmed by the existence of an ecient translation from DLP to plain disjunctive logic programming. Using this translation, an advanced KR system supporting the DLP language has been implemented on top of ...

The Smodels system is one of the state-of-the-art implementations of stable model computation for normal logic programs. In order to enable more realistic applications, the basic modeling language of normal programs has been extended with new constructs including cardinality and weight constraints and corresponding implementation techniques have been developed. This paper summarizes the extensions that have been included in the system, demonstrates their use, provides basic application methodology, illustrates the current level of performance of the system, and compares it to state-of-the-art satis ability checkers.

Logic programs with ordered disjunction (LPODs) combine ideas underlying Qualitative Choice Logic (Brewka, Benferhat, & Le Berre 2002) and answer set programming. Logic programming under answer set semantics is extended with a new connective called ordered disjunction. The new connective allows us to represent alternative, ranked options for problem solutions in the heads of rules: A × B intuitively means: if possible A, but if A is not possible then at least B. The semantics of logic programs...

We present a formal model of argumentation based on situation calculus which captures both the logical and the procedural aspects of argumentation processes. The logic is used to determine what is accepted by each agent participating in the discussion and by the group as a whole, on the basis of the speech acts performed during argumentation. Argumentation protocols, also called rules of order, describe declaratively which speech acts are legal in a particular state of the argumentation. We first discuss argumentation with fixed rules of order. Our model tolerates protocol violations but makes it possible to object to illegal actions. In realistic settings the rules of order themselves can at any time become the topic of the debate. We show how meta level argumentation of this kind can be modelled in what we call dynamic argument systems. To illustrate the notions introduced in the paper we present a reconstruction of Rescher's theory of formal disputation and a dynamic argument system with three levels which we use to discuss a murder case. 1

INTRODUCTION In nonmonotonic reasoning conflicts among defaults are ubiquitous. For instance, more specific rules may be in conflict with more general ones, a problem which has been studied intensively in the context of inheritance networks (Poole,1985; Touretzky, 1986; Touretzky et al., 1991). When defaults are used for representing design goals in configuration tasks conflicts naturally arise. The same is true in model based diagnosis where defaults are used to represent the assumption that components typically are ok. In legal reasoning conflicts among rules are very common (Prakken, 1993) and keep many lawyers busy (and rich). The standard nonmontonicformalisms handle such conflicts by generating multiple belief sets. In default logic (Reiter, 1980) and autoepistemic logic (Moore, 1985) these sets are called extensions or expansions, respectively. In circumscription (McCarthy, 1980) the belief sets correspond to different classes of preferred models. Usually, not all of the beli

We show how to compile programs formalizing update plus preference reasoning into standard generalized logic programs and show the correctness of the transformation.

A generalization of logic program rules is proposed where rules are built from weight constraints with type information for each predicate instead of simple literals. These kinds of constraints are useful for concisely representing different kinds of choices as well as cardinality, cost and resource constraints in combinatorial problems such as product configuration. A declarative semantics for the rules is presented which generalizes the stable model semantics of normal logic programs. It is shown that for ground rules the complexity of the relevant decision problems stays in NP. The fust implementation of the language handles a decidable subset where function symbols are not allowed. It is based on a new procedure for computing stable models for ground rules extending normal programs with choice and weight constructs and a compilation technique where a weight rule with variables is transformed to a set of such simpler ground rules.