scholarly journals Constraint Answer Set Programming: Integrational and Translational (or SMT-based) Approaches

2021 ◽  
pp. 1-31
Author(s):  
YULIYA LIERLER
Keyword(s):  
Automated Reasoning ◽  
Hybrid Approach ◽  
Answer Set Programming ◽  
Satisfiability Modulo Theories ◽  
Declarative Programming ◽  
Scheduling Problems ◽  
New Horizons ◽  
Research Areas ◽  
Constraint Processing ◽  
Answer Set

Abstract Constraint answer set programming or CASP, for short, is a hybrid approach in automated reasoning putting together the advances of distinct research areas such as answer set programming, constraint processing, and satisfiability modulo theories. CASP demonstrates promising results, including the development of a multitude of solvers: acsolver, clingcon, ezcsp, idp, inca, dingo, mingo, aspmt2smt, clingo[l,dl], and ezsmt. It opens new horizons for declarative programming applications such as solving complex train scheduling problems. Systems designed to find solutions to constraint answer set programs can be grouped according to their construction into, what we call, integrational or translational approaches. The focus of this paper is an overview of the key ingredients of the design of constraint answer set solvers drawing distinctions and parallels between integrational and translational approaches. The paper also provides a glimpse at the kind of programs its users develop by utilizing a CASP encoding of Traveling Salesman problem for illustration. In addition, we place the CASP technology on the map among its automated reasoning peers as well as discuss future possibilities for the development of CASP.

scholarly journals Train Scheduling with Hybrid Answer Set Programming

2020 ◽  
pp. 1-31
Author(s):  
DIRK ABELS ◽  
JULIAN JORDI ◽  
MAX OSTROWSKI ◽  
TORSTEN SCHAUB ◽  
AMBRA TOLETTI ◽  
...  
Keyword(s):  
Real World ◽  
Large Scale ◽  
Hybrid Approach ◽  
Answer Set Programming ◽  
Scheduling Problems ◽  
Planning And Scheduling ◽  
Train Scheduling ◽  
Fine Grained ◽  
Difference Constraints ◽  
Answer Set

Abstract We present a solution to real-world train scheduling problems, involving routing, scheduling, and optimization, based on Answer Set Programming (ASP). To this end, we pursue a hybrid approach that extends ASP with difference constraints to account for a fine-grained timing. More precisely, we exemplarily show how the hybrid ASP system clingo[DL] can be used to tackle demanding planning and scheduling problems. In particular, we investigate how to boost performance by combining distinct ASP solving techniques, such as approximations and heuristics, with preprocessing and encoding techniques for tackling large-scale, real-world train-scheduling instances.

scholarly journals On relation between constraint answer set programming and satisfiability modulo theories

2017 ◽  
Vol 17 (4) ◽  
pp. 559-590
Author(s):  
YULIYA LIERLER ◽  
BENJAMIN SUSMAN
Keyword(s):  
Answer Set Programming ◽  
Research Direction ◽  
Linear Constraints ◽  
Satisfiability Modulo Theories ◽  
Research Areas ◽  
Formal Relation ◽  
Answer Sets ◽  
Theoretical Foundations ◽  
Cross Fertilization ◽  
Answer Set

AbstractConstraint answer set programming is a promising research direction that integrates answer set programming with constraint processing. It is often informally related to the field of satisfiability modulo theories. Yet, the exact formal link is obscured as the terminology and concepts used in these two research areas differ. In this paper, we connect these two research areas by uncovering the precise formal relation between them. We believe that this work will boost the cross-fertilization of the theoretical foundations and the existing solving methods in both areas. As a step in this direction, we provide a translation from constraint answer set programs with integer linear constraints to satisfiability modulo linear integer arithmetic that paves the way to utilizing modern satisfiability modulo theories solvers for computing answer sets of constraint answer set programs.

scholarly journals A Formal Approach for Cautious Reasoning in Answer Set Programming (Extended Abstract)

2020 ◽  
Author(s):  
Giovanni Amendola ◽  
Carmine Dodaro ◽  
Marco Maratea
Keyword(s):  
Answer Set Programming ◽  
Satisfiability Modulo Theories ◽  
Propositional Satisfiability ◽  
Formal Approach ◽  
Boolean Formulas ◽  
Reasoning Tasks ◽  
Answer Set

The issue of describing in a formal way solving algorithms in various fields such as Propositional Satisfiability (SAT), Quantified SAT, Satisfiability Modulo Theories, Answer Set Programming (ASP), and Constraint ASP, has been relatively recently solved employing abstract solvers. In this paper we deal with cautious reasoning tasks in ASP, and design, implement and test novel abstract solutions, borrowed from backbone computation in SAT. By employing abstract solvers, we also formally show that the algorithms for solving cautious reasoning tasks in ASP are strongly related to those for computing backbones of Boolean formulas. Some of the new solutions have been implemented in the ASP solver WASP, and tested.

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 A Case for Query-driven Predicate Answer Set Programming

10.29007/ngm2 ◽  
2018 ◽  
Author(s):  
Gopal Gupta ◽  
Elmer Salazar ◽  
Kyle Marple ◽  
Zhuo Chen ◽  
Farhad Shakerin
Keyword(s):  
Logic Programming ◽  
Common Sense ◽  
Automated Reasoning ◽  
Answer Set Programming ◽  
Stable Model ◽  
Common Sense Reasoning ◽  
Knowledge Based ◽  
Full Power ◽  
Programming Systems ◽  
Answer Set

Answer Set Programming (ASP) has emerged as a successful paradigm for developing intelligent applications. ASP is based on adding negation as failure to logic programming under the stable model semantics regime. ASP allows for sophisticated reasoning mechanisms that are employed by humans to be modeled elegantly. We argue that being able to model common sense reasoning as used by humans is critical for success of automated reasoning. We also argue that extending answer programming systems to general predicates is critical to realizing the full power of ASP. Goal-directed predicate ASP systems are needed to make the ASP technology practical for building large, scalable knowledge-based applications.

scholarly journals The Sixth Answer Set Programming Competition

2017 ◽  
Vol 60 ◽  
pp. 41-95 ◽  
Author(s):  
Martin Gebser ◽  
Marco Maratea ◽  
Francesco Ricca
Keyword(s):  
Logic Programming ◽  
Related Problem ◽  
Answer Set Programming ◽  
Declarative Programming ◽  
Modeling Language ◽  
Design Decisions ◽  
Planning And Scheduling ◽  
The University ◽  
Selection Stage ◽  
Answer Set

Answer Set Programming (ASP) is a well-known paradigm of declarative programming with roots in logic programming and non-monotonic reasoning. Similar to other closely related problem-solving technologies, such as SAT/SMT, QBF, Planning and Scheduling, advancements in ASP solving are assessed in competition events. In this paper, we report about the design and results of the Sixth ASP Competition, which was jointly organized by the University of Calabria (Italy), Aalto University (Finland), and the University of Genoa (Italy), in affiliation with the 13th International Conference on Logic Programming and Non-Monotonic Reasoning. This edition maintained some of the design decisions introduced in 2014, e.g., the conception of sub-tracks, the scoring scheme, and the adherence to a fixed modeling language in order to push the adoption of the ASP-Core-2 standard. On the other hand, it featured also some novelties, like a benchmark selection stage classifying instances according to their empirical hardness, and a "Marathon" track where the top-performing systems are given more time for solving hard benchmarks.

scholarly journals Fuzzy answer set computation via satisfiability modulo theories

2015 ◽  
Vol 15 (4-5) ◽  
pp. 588-603 ◽  
Author(s):  
MARIO ALVIANO ◽  
RAFAEL PEÑALOZA
Keyword(s):  
Fuzzy Logic ◽  
Structural Properties ◽  
Answer Set Programming ◽  
Satisfiability Modulo Theories ◽  
Prototype System ◽  
Imprecise Information ◽  
Fuzzy Connectives ◽  
Answer Set

AbstractFuzzy answer set programming (FASP) combines two declarative frameworks, answer set programming and fuzzy logic, in order to model reasoning by default over imprecise information. Several connectives are available to combine different expressions; in particular the Gödel and Łukasiewicz fuzzy connectives are usually considered, due to their properties. Although the Gödel conjunction can be easily eliminated from rule heads, we show through complexity arguments that such a simplification is infeasible in general for all other connectives. The paper analyzes a translation of FASP programs into satisfiability modulo theories (SMT), which in general produces quantified formulas because of the minimality of the semantics. Structural properties of many FASP programs allow to eliminate the quantification, or to sensibly reduce the number of quantified variables. Indeed, integrality constraints can replace recursive rules commonly used to force Boolean interpretations, and completion subformulas can guarantee minimality for acyclic programs with atomic heads. Moreover, head cycle free rules can be replaced by shifted subprograms, whose structure depends on the eliminated head connective, so that ordered completion may replace the minimality check if also Łukasiewicz disjunction in rule bodies is acyclic. The paper also presents and evaluates a prototype system implementing these translations.

Answer Sets and the Language of Answer Set Programming

AI Magazine ◽  
2016 ◽  
Vol 37 (3) ◽  
pp. 7-12 ◽  
Author(s):  
Vladimir Lifschitz
Keyword(s):  
Answer Set Programming ◽  
Declarative Programming ◽  
Logic Programs ◽  
Special Issue ◽  
Programming Paradigm ◽  
Introductory Article ◽  
Answer Set Semantics ◽  
Answer Sets ◽  
Semantics Of Logic Programs ◽  
Answer Set

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.

scholarly journals A translational approach to constraint answer set solving

2010 ◽  
Vol 10 (4-6) ◽  
pp. 465-480 ◽  
Author(s):  
CHRISTIAN DRESCHER ◽  
TOBY WALSH
Keyword(s):  
Answer Set Programming ◽  
Constraint Satisfaction Problems ◽  
Logic Programs ◽  
New Approach ◽  
Constraint Processing ◽  
Translational Approach ◽  
Unit Propagation ◽  
Processing Techniques ◽  
Finite Domains ◽  
Answer Set

AbstractWe present a new approach to enhancing Answer Set Programming (ASP) with Constraint Processing techniques which allows for solving interesting Constraint Satisfaction Problems in ASP. We show how constraints on finite domains can be decomposed into logic programs such that unit-propagation achieves arc, bound or range consistency. Experiments with our encodings demonstrate their computational impact.

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