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 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 Lazy Model Expansion: Interleaving Grounding with Search

2015 ◽  
Vol 52 ◽  
pp. 235-286 ◽  
Author(s):  
Broes De Cat ◽  
Marc Denecker ◽  
Maurice Bruynooghe ◽  
Peter Stuckey
Keyword(s):  
Knowledge Representation ◽  
Blow Up ◽  
State Of The Art ◽  
Search Algorithm ◽  
Model Generation ◽  
Partial Assignment ◽  
Complete Assignment ◽  
Knowledge Representation Language ◽  
Representation Language ◽  
Generation Problem

Finding satisfying assignments for the variables involved in a set of constraints can be cast as a (bounded) model generation problem: search for (bounded) models of a theory in some logic. The state-of-the-art approach for bounded model generation for rich knowledge representation languages like ASP and FO(.) and a CSP modeling language such as Zinc, is ground-and-solve: reduce the theory to a ground or propositional one and apply a search algorithm to the resulting theory. An important bottleneck is the blow-up of the size of the theory caused by the grounding phase. Lazily grounding the theory during search is a way to overcome this bottleneck. We present a theoretical framework and an implementation in the context of the FO(.) knowledge representation language. Instead of grounding all parts of a theory, justifications are derived for some parts of it. Given a partial assignment for the grounded part of the theory and valid justifications for the formulas of the non-grounded part, the justifications provide a recipe to construct a complete assignment that satisfies the non-grounded part. When a justification for a particular formula becomes invalid during search, a new one is derived; if that fails, the formula is split in a part to be grounded and a part that can be justified. Experimental results illustrate the power and generality of this approach.

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 Declarative encodings of acyclicity properties

2015 ◽  
Vol 30 (4) ◽  
pp. 923-952
Author(s):  
Martin Gebser ◽  
Tomi Janhunen ◽  
Jussi Rintanen
Keyword(s):  
Linear Programming ◽  
Knowledge Representation ◽  
Structural Properties ◽  
Propositional Logic ◽  
Answer Set Programming ◽  
Tree Structures ◽  
Computational Performance ◽  
Primary Representation ◽  
Representation Language ◽  
Answer Set

Abstract Many knowledge representation tasks involve trees or similar structures as abstract datatypes. However, devising compact and efficient declarative representations of such structural properties is non-obvious and can be challenging indeed. In this article, we take a number of acyclicity properties into consideration and investigate various logic-based approaches to encode them. We use answer set programming as the primary representation language but also consider mappings to related formalisms, such as propositional logic, difference logic and linear programming. We study the compactness of encodings and the resulting computational performance on benchmarks involving acyclic or tree structures.

scholarly journals lpopt: A Rule Optimization Tool for Answer Set Programming

2020 ◽  
Vol 177 (3-4) ◽  
pp. 275-296
Author(s):  
Manuel Bichler ◽  
Michael Morak ◽  
Stefan Woltran
Keyword(s):  
Logic Programming ◽  
Experimental Evaluation ◽  
State Of The Art ◽  
Answer Set Programming ◽  
Weak Constraints ◽  
Rule Optimization ◽  
Theoretical Foundations ◽  
Arithmetic Expressions ◽  
Answer Set

State-of-the-art answer set programming (ASP) solvers rely on a program called a grounder to convert non-ground programs containing variables into variable-free, propositional programs. The size of this grounding depends heavily on the size of the non-ground rules, and thus, reducing the size of such rules is a promising approach to improve solving performance. To this end, in this paper we announce lpopt, a tool that decomposes large logic programming rules into smaller rules that are easier to handle for current solvers. The tool is specifically tailored to handle the standard syntax of the ASP language (ASP-Core) and makes it easier for users to write efficient and intuitive ASP programs, which would otherwise often require significant handtuning by expert ASP engineers. It is based on an idea proposed by Morak and Woltran (2012) that we extend significantly in order to handle the full ASP syntax, including complex constructs like aggregates, weak constraints, and arithmetic expressions. We present the algorithm, the theoretical foundations on how to treat these constructs, as well as an experimental evaluation showing the viability of our approach.

scholarly journals The third open answer set programming competition

2012 ◽  
Vol 14 (1) ◽  
pp. 117-135 ◽  
Author(s):  
FRANCESCO CALIMERI ◽  
GIOVAMBATTISTA IANNI ◽  
FRANCESCO RICCA
Keyword(s):  
State Of The Art ◽  
Answer Set Programming ◽  
Benchmark Problems ◽  
Declarative Programming ◽  
The Third ◽  
Real World Applications ◽  
Close Relationship ◽  
The University ◽  
Sat Modulo Theories ◽  
Answer Set

AbstractAnswer Set Programming (ASP) is a well-established paradigm of declarative programming in close relationship with other declarative formalisms such as SAT Modulo Theories, Constraint Handling Rules, FO(.), PDDL and many others. Since its first informal editions, ASP systems have been compared in the now well-established ASP Competition. The Third (Open) ASP Competition, as the sequel to the ASP Competitions Series held at the University of Potsdam in Germany (2006–2007) and at the University of Leuven in Belgium in 2009, took place at the University of Calabria (Italy) in the first half of 2011. Participants competed on a pre-selected collection of benchmark problems, taken from a variety of domains as well as real world applications. The Competition ran on two tracks: the Model and Solve (M&S) Track, based on an open problem encoding, and open language, and open to any kind of system based on a declarative specification paradigm; and the System Track, run on the basis of fixed, public problem encodings, written in a standard ASP language. This paper discusses the format of the competition and the rationale behind it, then reports the results for both tracks. Comparison with the second ASP competition and state-of-the-art solutions for some of the benchmark domains is eventually discussed.

Finding optimal plans for multiple teams of robots through a mediator: A logic-based approach

2013 ◽  
Vol 13 (4-5) ◽  
pp. 831-846 ◽  
Author(s):  
ESRA ERDEM ◽  
VOLKAN PATOGLU ◽  
ZEYNEP G. SARIBATUR ◽  
PETER SCHÜLLER ◽  
TANSEL URAS
Keyword(s):  
Computational Complexity ◽  
Energy Efficient ◽  
State Of The Art ◽  
Answer Set Programming ◽  
The State ◽  
Action Languages ◽  
Mathematical Definition ◽  
Definition Of ◽  
Answer Set

AbstractWe study the problem of finding optimal plans for multiple teams of robots through a mediator, where each team is given a task to complete in its workspace on its own and where teams are allowed to transfer robots between each other, subject to the following constraints: 1) teams (and the mediator) do not know about each other's workspace or tasks (e.g., for privacy purposes); 2) every team can lend or borrow robots, but not both (e.g., transportation/calibration of robots between/for different workspaces is usually costly). We present a mathematical definition of this problem and analyze its computational complexity. We introduce a novel, logic-based method to solve this problem, utilizing action languages and answer set programming for representation, and the state-of-the-art ASP solvers for reasoning. We show the applicability and usefulness of our approach by experiments on various scenarios of responsive and energy-efficient cognitive factories.

scholarly journals Meta-Interpretive Learning Using HEX-Programs

2019 ◽  
Author(s):  
Tobias Kaminski ◽  
Thomas Eiter ◽  
Katsumi Inoue
Keyword(s):  
Logic Programming ◽  
Inductive Logic Programming ◽  
Inductive Logic ◽  
Answer Set Programming ◽  
Search Space ◽  
Recent Approach ◽  
Performance Gains ◽  
Pruning Techniques ◽  
Answer Set

Meta-Interpretive Learning (MIL) is a recent approach for Inductive Logic Programming (ILP) implemented in Prolog. Alternatively, MIL-problems can be solved by using Answer Set Programming (ASP), which may result in performance gains due to efficient conflict propagation. However, a straightforward MIL-encoding results in a huge size of the ground program and search space. To address these challenges, we encode MIL in the HEX-extension of ASP, which mitigates grounding issues, and we develop novel pruning techniques.

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