In this article, we give an overview of the answer set programming paradigm, explain its strengths, and illustrate its main features in terms of examples and an application problem. In this article, we give an overview of the answer set programming paradigm, explain its strengths, and illustrate its main features in terms of examples and an application problem.

Answer set programming (ASP) has emerged as an approach to declarative problem solving based on the stable model semantics for logic programs. The basic idea is to represent a computational problem by a logic program, formulating constraints in terms of rules, such that its answer sets correspond to problem solutions. Compact problem representations take advantage of genuine modeling features of ASP, including (first-order) variables, negation by default, and recursion. In this article, we demonstrate the ASP methodology on two example scenarios, illustrating basic as well as advanced modeling and solving concepts.

ASP has been applied fruitfully to a wide range of areas in AI and in other fields, both in academia and in industry, thanks to the expressive representation languages of ASP and the continuous improvement of ASP solvers. We present some of these ASP applications, in particular, in knowledge representation and reasoning, robotics, bioinformatics and computational biology as well as some industrial applications. We discuss the challenges addressed by ASP in these applications and emphasize the strengths of ASP as a useful AI paradigm.

Answer set programming is a declarative programming paradigm based on the answer set semantics of logic programs. This introductory article provides the mathematical background for the discussion of answer set programming in other contributions to this special issue.

Answer set programming is a declarative problem solving paradigm that rests upon a workflow involving modeling, grounding, and solving. While the former is described by Gebser and Schaub (2016), we focus here on key issues in grounding, or how to systematically replace object variables by ground terms in a effective way, and solving, or how to compute the answer sets of a propositional logic program obtained by grounding.

The goal of this article is threefold. First, we trace the history of the development of answer set solvers, by accounting for more than a dozen of them. Second, we discuss development tools and environments that facilitate the use of answer set programming technology in practical applications. Last, we present the evolution of the answer set programming competitions, prime venues for tracking advances in answer set solving technology.

This editorial introduces answer set programming, a vibrant research area in computational knowledge representation and declarative programming. We give a brief overview of the articles that form this special issue on answer set programming and of the main topics they discuss.

In answer-set programming (ASP), programs can be viewed as specifications of finite Herbrand structures. Other logics can be (and, in fact, were) used towards the same end and can be taken as the basis of declarative programming systems of similar functionality as ASP. We discuss here one such logic, the logic FO(ID), and its implementation IDP3. The choice is motivated by notable similarities between ASP and FO(ID), even if both approaches trace back to different origins

The answer sets for the given program can then be computed by special software systems called answer set solvers, such as DLV, Smodels, or clasp. It is especially relevant to language processing and understanding, learning, reasoning with so called defaults -- statements of the theory update/revision, preferences, diagnosis, form "Normally (typically, as a rule) elements of class description logics, semantic web, multicontext systems, C have property P." We all learn rather early in life and argumentation. Other areas that include that parents normally love their children, citizens are applications of ASP are, for instance, computational normally required to pay taxes, and so forth. We also biology, systems biology, bioinformatics, automatic learn, however, that these rules are not absolute and music composition, assisted living, software engineering, allow various types of exceptions. It is natural to bounded model checking, and robotics. Learning correct ways to decision support systems (Nogueira et al. 2001) (used reason with defaults and their exceptions is necessary by United Space Alliance), automated product configuration for building an agent capable of using such a KB. One (Tiihonen, Soininen, and Sulonen 2003) of the best available solutions to this problem uses (used by Variantum Oy), intelligent call routing the knowledge representation language CR-Prolog (Leone and Ricca 2015) (used by Italia Telecom) and (Balduccini and Gelfond 2003) -- a simple extension configuration and reconfiguration of railway safety of the original ASP language of logic programs with systems (Aschinger et al. 2011) (used by Siemens).