Using Answer Set Programming for Knowledge Representation and Reasoning: Future Directions

2008 ◽  
pp. 69-70
Author(s):  
Chitta Baral
Keyword(s):  
Knowledge Representation ◽  
Answer Set Programming ◽  
Knowledge Representation And Reasoning ◽  
Future Directions ◽  
Answer Set

scholarly journals Tractable answer-set programming with weight constraints: bounded treewidth is not enough

2012 ◽  
Vol 14 (2) ◽  
pp. 141-164 ◽  
Author(s):  
REINHARD PICHLER ◽  
STEFAN RÜMMELE ◽  
STEFAN SZEIDER ◽  
STEFAN WOLTRAN
Keyword(s):  
Knowledge Representation ◽  
Linear Time ◽  
Answer Set Programming ◽  
Knowledge Representation And Reasoning ◽  
Cardinality Constraints ◽  
Bounded Treewidth ◽  
Straightforward Application ◽  
Intractable Problems ◽  
Important Extension ◽  
Answer Set

AbstractCardinality constraints or, more generally, weight constraints are well recognized as an important extension of answer-set programming. Clearly, all common algorithmic tasks related to programs with cardinality or weight constraints – like checking the consistency of a program – are intractable. Many intractable problems in the area of knowledge representation and reasoning have been shown to become linear time tractable if the treewidth of the programs or formulas under consideration is bounded by some constant. The goal of this paper is to apply the notion of treewidth to programs with cardinality or weight constraints and to identify tractable fragments. It will turn out that the straightforward application of treewidth to such class of programs does not suffice to obtain tractability. However, by imposing further restrictions, tractability can be achieved.

scholarly journals Applications of Answer Set Programming

AI Magazine ◽  
2016 ◽  
Vol 37 (3) ◽  
pp. 53-68 ◽  
Author(s):  
Esra Erdem ◽  
Michael Gelfond ◽  
Nicola Leone
Keyword(s):  
Computational Biology ◽  
Knowledge Representation ◽  
Continuous Improvement ◽  
Answer Set Programming ◽  
Industrial Applications ◽  
Knowledge Representation And Reasoning ◽  
Wide Range ◽  
Answer Set

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.

scholarly journals Evaluation Techniques and Systems for Answer Set Programming: a Survey

2018 ◽  
Author(s):  
Martin Gebser ◽  
Nicola Leone ◽  
Marco Maratea ◽  
Simona Perri ◽  
Francesco Ricca ◽  
...  
Keyword(s):  
Knowledge Representation ◽  
Answer Set Programming ◽  
Knowledge Representation And Reasoning ◽  
Modeling Language ◽  
Scientific Applications ◽  
Two Factors ◽  
History Of ◽  
Evaluation Techniques ◽  
Answer Set

Answer set programming (ASP) is a prominent knowledge representation and reasoning paradigm that found both industrial and scientific applications. The success of ASP is due to the combination of two factors: a rich modeling language and the availability of efficient ASP implementations. In this paper we trace the history of ASP systems, describing the key evaluation techniques and their implementation in actual tools.

scholarly journals ASP-Core-2 Input Language Format

2019 ◽  
Vol 20 (2) ◽  
pp. 294-309 ◽  
Author(s):  
FRANCESCO CALIMERI ◽  
WOLFGANG FABER ◽  
MARTIN GEBSER ◽  
GIOVAMBATTISTA IANNI ◽  
ROLAND KAMINSKI ◽  
...  
Keyword(s):  
Knowledge Representation ◽  
Answer Set Programming ◽  
Knowledge Representation And Reasoning ◽  
Input Language ◽  
Main Driver ◽  
Answer Set

AbstractStandardization of solver input languages has been a main driver for the growth of several areas within knowledge representation and reasoning, fostering the exploitation in actual applications. In this document, we present the ASP-CORE-2 standard input language for Answer Set Programming, which has been adopted in ASP Competition events since 2013.

scholarly journals Answer Set Programming: An Introduction to the Special Issue

AI Magazine ◽  
2016 ◽  
Vol 37 (3) ◽  
pp. 5-6 ◽  
Author(s):  
Gerhard Brewka ◽  
Thomas Eiter ◽  
Miroslaw Truszczynski
Keyword(s):  
Knowledge Representation ◽  
Answer Set Programming ◽  
Research Area ◽  
Declarative Programming ◽  
Special Issue ◽  
Answer Set

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.

scholarly journals Advancing Lazy-Grounding ASP Solving Techniques – Restarts, Phase Saving, Heuristics, and More

2020 ◽  
Vol 20 (5) ◽  
pp. 609-624
Author(s):  
ANTONIUS WEINZIERL ◽  
RICHARD TAUPE ◽  
GERHARD FRIEDRICH
Keyword(s):  
Knowledge Representation ◽  
State Of The Art ◽  
Answer Set Programming ◽  
Negative Effects ◽  
First Time ◽  
Domain Independent ◽  
Large Improvement ◽  
Answer Set ◽  
Art Techniques

AbstractAnswer-Set Programming (ASP) is a powerful and expressive knowledge representation paradigm with a significant number of applications in logic-based AI. The traditional ground-and-solve approach, however, requires ASP programs to be grounded upfront and thus suffers from the so-called grounding bottleneck (i.e., ASP programs easily exhaust all available memory and thus become unsolvable). As a remedy, lazy-grounding ASP solvers have been developed, but many state-of-the-art techniques for grounded ASP solving have not been available to them yet. In this work we present, for the first time, adaptions to the lazy-grounding setting for many important techniques, like restarts, phase saving, domain-independent heuristics, and learned-clause deletion. Furthermore, we investigate their effects and in general observe a large improvement in solving capabilities and also uncover negative effects in certain cases, indicating the need for portfolio solving as known from other solvers.

scholarly journals Thedlvhexsystem for knowledge representation: recent advances (system description)

2016 ◽  
Vol 16 (5-6) ◽  
pp. 866-883 ◽  
Author(s):  
CHRISTOPH REDL
Keyword(s):  
Knowledge Representation ◽  
Real World ◽  
Answer Set Programming ◽  
Current Status ◽  
System Description ◽  
Recent Advances ◽  
Real World Applications ◽  
External Sources ◽  
Answer Set

AbstractThedlvhexsystem implements thehex-semantics, which integrates answer set programming (ASP) with arbitrary external sources. Since its first release ten years ago, significant advancements were achieved. Most importantly, the exploitation of properties of external sources led to efficiency improvements and flexibility enhancements of the language, and technical improvements on the system side increased user's convenience. In this paper, we present the current status of the system and point out the most important recent enhancements over early versions. While existing literature focuses on theoretical aspects and specific components, a bird's eye view of the overall system is missing. In order to promote the system for real-world applications, we further present applications which were already successfully realized on top ofdlvhex.

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