scholarly journals Evaluating Epistemic Negation in Answer Set Programming (Extended Abstract)

2017 ◽  
Author(s):  
Yi-Dong Shen ◽  
Thomas Eiter
Keyword(s):  
Knowledge Representation ◽  
Nonmonotonic Reasoning ◽  
Answer Set Programming ◽  
Modal Operator ◽  
World Views ◽  
Epistemic Modal ◽  
Standing Problem ◽  
Answer Set

Epistemic negation 'not' along with default negation 'neg' plays a key role in knowledge representation and nonmonotonic reasoning. However, the existing approaches behave not satisfactorily in that they suffer from the problems of unintended world views due to recursion through the epistemic modal operator K or M ( K F and M F are shorthands for (neg not F) and (not neg F), respectively). In this paper we present a general approach to epistemic negation which is free of unintended world views and thus offers a solution to the long-standing problem of epistemic specifications which were introduced by Gelfond 1991 over two decades ago.

scholarly journals Reasoning about Cardinal Directions between 3-Dimensional Extended Objects using Answer Set Programming

2020 ◽  
Vol 20 (6) ◽  
pp. 942-957
Author(s):  
Yusuf Izmirlioglu ◽  
Esra Erdem
Keyword(s):  
Nonmonotonic Reasoning ◽  
Answer Set Programming ◽  
3 Dimensional ◽  
3D Space ◽  
Formal Framework ◽  
Different Types ◽  
New Type ◽  
Cardinal Directions ◽  
Answer Set

AbstractWe propose a novel formal framework (called 3D-NCDC-ASP) to represent and reason about cardinal directions between extended objects in 3-dimensional (3D) space, using Answer Set Programming (ASP). 3D-NCDC-ASP extends Cardinal Directional Calculus (CDC) with a new type of default constraints, and NCDC-ASP to 3D. 3D-NCDC-ASP provides a flexible platform offering different types of reasoning: Nonmonotonic reasoning with defaults, checking consistency of a set of constraints on 3D cardinal directions between objects, explaining inconsistencies, and inferring missing CDC relations. We prove the soundness of 3D-NCDC-ASP, and illustrate its usefulness with applications.

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.

scholarly journals Enhancing Lazy Grounding with Lazy Normalization in Answer-Set Programming

2019 ◽  
Vol 33 ◽  
pp. 2694-2702
Author(s):  
Jori Bomanson ◽  
Tomi Janhunen ◽  
Antonius Weinzierl
Keyword(s):  
Asymptotic Behavior ◽  
Knowledge Representation ◽  
Answer Set Programming ◽  
Expressive Power ◽  
Rule Based ◽  
On Demand ◽  
Space And Time ◽  
Different Types ◽  
Representation Formalism ◽  
Answer Set

Answer-Set Programming (ASP) is an expressive rule-based knowledge-representation formalism. Lazy grounding is a solving technique that avoids the well-known grounding bottleneck of traditional ASP evaluation but is restricted to normal rules, severely limiting its expressive power. In this work, we introduce a framework to handle aggregates by normalizing them on demand during lazy grounding, hence relieving the restrictions of lazy grounding significantly. We term our approach as lazy normalization and demonstrate its feasibility for different types of aggregates. Asymptotic behavior is analyzed and correctness of the presented lazy normalizations is shown. Benchmark results indicate that lazy normalization can bring up-to exponential gains in space and time as well as enable ASP to be used in new application areas.

scholarly journals The Seventh Answer Set Programming Competition: Design and Results

2019 ◽  
Vol 20 (2) ◽  
pp. 176-204 ◽  
Author(s):  
MARTIN GEBSER ◽  
MARCO MARATEA ◽  
FRANCESCO RICCA
Keyword(s):  
Knowledge Representation ◽  
Logic Programming ◽  
State Of The Art ◽  
Answer Set Programming ◽  
The State ◽  
International Conference ◽  
Knowledge Representation Language ◽  
Representation Language ◽  
The University ◽  
Answer Set

AbstractAnswer Set Programming (ASP) is a prominent knowledge representation language with roots in logic programming and non-monotonic reasoning. Biennial ASP competitions are organized in order to furnish challenging benchmark collections and assess the advancement of the state of the art in ASP solving. In this paper, we report on the design and results of the Seventh ASP Competition, jointly organized by the University of Calabria (Italy), the University of Genova (Italy), and the University of Potsdam (Germany), in affiliation with the 14th International Conference on Logic Programming and Non-Monotonic Reasoning (LPNMR 2017).

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 Exploiting Justifications for Lazy Grounding of Answer Set Programs

2018 ◽  
Author(s):  
Bart Bogaerts ◽  
Antonius Weinzierl
Keyword(s):  
Knowledge Representation ◽  
Answer Set Programming ◽  
Search Tree ◽  
Top Down ◽  
Clause Learning ◽  
Speed Up ◽  
Representation Formalism ◽  
Answer Set

Answer set programming (ASP) is an established knowledge representation formalism. Lazy grounding avoids the so-called grounding bottleneck of ASP by interleaving grounding and solving; this technique was recently extended to work with conflict-driven clause learning. Unfortunately, it often happens that such a lazy grounding ASP system, at the fixpoint of the evaluation, arrives at an assignment that contains literals that are true but unjustified. The system then is unable to determine the actual causes of the situation and falls back to chronological backtracking, potentially wasting an exponential amount of time. In this paper, we show how top-down query mechanisms can be used to analyze the situation, learn a new clause or nogood, and backjump further in the search tree. Contributions include a rephrasing of lazy grounding in terms of justifications and algorithms to construct relevant justifications without grounding. Initial experiments indicate that the newly developed techniques indeed allow for an exponential speed-up.

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