Answer Set Programming - Explore the Science & Experts | ideXlab

Scan Science and Technology

Contact Leading Edge Experts & Companies

Answer Set Programming

The Experts below are selected from a list of 11343 Experts worldwide ranked by ideXlab platform

Answer Set Programming – Free Register to Access Experts & Abstracts

Miroslaw Truszczynski – One of the best experts on this subject based on the ideXlab platform.

  • The Informal Semantics of Answer Set Programming: A Tarskian Perspective
    arXiv: Artificial Intelligence, 2019
    Co-Authors: Marc Denecker, Yuliya Lierler, Miroslaw Truszczynski, Joost Vennekens


    In Knowledge Representation, it is crucial that knowledge engineers have a good understanding of the formal expressions that they write. What formal expressions state intuitively about the domain of discourse is studied in the theory of the informal semantics of a logic. In this paper we study the informal semantics of Answer Set Programming. The roots of Answer Set Programming lie in the language of Extended Logic Programming, which was introduced initially as an epistemic logic for default and autoepistemic reasoning. In 1999, the seminal papers on Answer Set Programming proposed to use this logic for a different purpose, namely, to model and solve search problems. Currently, the language is used primarily in this new role. However, the original epistemic intuitions lose their explanatory relevance in this new context. How Answer Set programs are connected to the specifications of problems they model is more easily explained in a classical Tarskian semantics, in which models correspond to possible worlds, rather than to belief states of an epistemic agent. In this paper, we develop a new theory of the informal semantics of Answer Set Programming, which is formulated in the Tarskian Setting and based on Frege’s compositionality principle. It differs substantially from the earlier epistemic theory of informal semantics, providing a different view on the meaning of the connectives in Answer Set Programming and on its relation to other logics, in particular classical logic.

  • Answer Set Programming: An Introduction to the Special Issue
    AI Magazine, 2016
    Co-Authors: Gerhard Brewka, Thomas Eiter, Miroslaw Truszczynski


    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.

  • Answer Set Programming at a glance
    Communications of the ACM, 2011
    Co-Authors: Gerhard Brewka, Thomas Eiter, Miroslaw Truszczynski


    The motivation and key concepts behind Answer Set Programming—a promising approach to declarative problem solving.

Torsten Schaub – One of the best experts on this subject based on the ideXlab platform.

  • Answer Set Programming unleashed!
    KI – Künstliche Intelligenz, 2018
    Co-Authors: Torsten Schaub, Stefan Woltran


    Answer Set Programming faces an increasing popularity for problem solving in various domains. While its modeling language allows us to express many complex problems in an easy way, its solving technology enables their effective resolution. In what follows, we detail some of the key factors of its success. Answer Set Programming [ASP; Brewka et al. Commun ACM 54(12):92–103, (2011)] is seeing a rapid proliferation in academia and industry due to its easy and flexible way to model and solve knowledge-intense combinatorial (optimization) problems. To this end, ASP offers a high-level modeling language paired with high-performance solving technology. As a result, ASP systems provide out-off-the-box, general-purpose search engines that allow for enumerating (optimal) solutions. They are represented as Answer Sets, each being a Set of atoms representing a solution. The declarative approach of ASP allows a user to concentrate on a problem’s specification rather than the computational means to solve it. This makes ASP a prime candidate for rapid prototyping and an attractive tool for teaching key AI techniques since complex problems can be expressed in a succinct and elaboration tolerant way. This is eased by the tuning of ASP’s modeling language to knowledge representation and reasoning (KRR). The resulting impact is nicely reflected by a growing range of successful applications of ASP [Erdem et al. AI Mag 37(3):53–68, 2016; Falkner et al. Industrial applications of Answer Set Programming. K++nstliche Intelligenz (2018)].

  • Design Space Exploration with Answer Set Programming
    Künstliche Intelligenz, 2018
    Co-Authors: Christian Haubelt, Kai Neubauer, Torsten Schaub, Philipp Wanko


    The aim of our project design space exploration with Answer Set Programming is to develop a general framework based on Answer Set Programming (ASP) that finds valid solutions to the system design problem and simultaneously performs Design Space Exploration (DSE) to find the most favorable alternatives. We leverage recent developments in ASP solving that allow for tight integration of background theories to create a holistic framework for effective DSE.

  • Temporal Answer Set Programming on Finite Traces
    arXiv: Artificial Intelligence, 2018
    Co-Authors: Pedro Cabalar, Torsten Schaub, Roland Kaminski, Anna Schuhmann


    In this paper, we introduce an alternative approach to Temporal Answer Set Programming that relies on a variation of Temporal Equilibrium Logic (TEL) for finite traces. This approach allows us to even out the expressiveness of TEL over infinite traces with the computational capacity of (incremental) Answer Set Programming (ASP). Also, we argue that finite traces are more natural when reasoning about action and change. As a result, our approach is readily implementable via multi-shot ASP systems and benefits from an extension of ASP’s full-fledged input language with temporal operators. This includes future as well as past operators whose combination offers a rich temporal modeling language. For computation, we identify the class of temporal logic programs and prove that it constitutes a normal form for our approach. Finally, we outline two implementations, a generic one and an extension of clingo.

Martin Gebser – One of the best experts on this subject based on the ideXlab platform.

  • Answer Set Programming modulo Acyclicity
    , 2015
    Co-Authors: Jori Bomanson, Martin Gebser, Toni Janhunen, Benjamin Kaufmann, Torsten Schaub


    Acyclicity constraints are prevalent in knowledge representation and, in particular, applications where acyclic data structures such as DAGs and trees play a role. Recently, such constraints have been considered in the satisfiability modulo theories (SMT) framework, and in this paper we carry out an analogous extension to the Answer Set Programming (ASP) paradigm. The resulting formalism, ASP modulo acyclicity, offers a rich Set of primitives to express constraints related with recursive structures. The implementation, obtained as an extension to the state-of-the-art Answer Set solver clasp, provides a unique combination of traditional unfounded Set checking with acyclicity propagation.

  • Shift-design with Answer Set Programming
    , 2015
    Co-Authors: Michael Abseher, Torsten Schaub, Martin Gebser, Nysret Musliu, Stefan Woltran


    Answer Set Programming (ASP) is a powerful declarative Programming paradigm that has been successfully applied to many dierent domains. Recently, ASP has also proved successful for hard optimization problems like course timetabling. In this paper, we approach another important task, namely, the shift design problem, aiming at an alignment of a minimum number of shifts in order to meet required numbers of employees (which typically vary for different time periods) in such a way that over- and understang is minimized. We provide an ASP encoding of the shift design problem, which, to the best of our knowledge, has not been addressed by ASP yet.

  • A System for Interactive Query Answering with Answer Set Programming
    arXiv: Artificial Intelligence, 2013
    Co-Authors: Martin Gebser, Philipp Obermeier, Torsten Schaub


    Reactive Answer Set Programming has paved the way for incorporating online information into operative solving processes. Although this technology was originally devised for dealing with data streams in dynamic environments, like assisted living and cognitive robotics, it can likewise be used to incorporate facts, rules, or queries provided by a user. As a result, we present the design and implementation of a system for interactive query Answering with reactive Answer Set Programming. Our system quontroller is based on the reactive solver oclingo and implemented as a dedicated front-end. We describe its functionality and implementation, and we illustrate its features by some selected use cases.