scholarly journals Boosting Answer Set Optimization with Weighted Comparator Networks

2020 ◽  
Vol 20 (4) ◽  
pp. 512-551
Author(s):  
JORI BOMANSON ◽  
TOMI JANHUNEN
Keyword(s):  
Infinite Family ◽  
Answer Set Programming ◽  
Set Optimization ◽  
Benchmark Problems ◽  
Effective Technique ◽  
Optimization Criteria ◽  
Knowledge Intensive ◽  
Problem Instances ◽  
Bound Optimization ◽  
Answer Set

AbstractAnswer set programming (ASP) is a paradigm for modeling knowledge-intensive domains and solving challenging reasoning problems. In ASP solving, a typical strategy is to preprocess problem instances by rewriting complex rules into simpler ones. Normalization is a rewriting process that removes extended rule types altogether in favor of normal rules. Recently, such techniques led to optimization rewriting in ASP, where the goal is to boost answer set optimization by refactoring the optimization criteria of interest. In this paper, we present a novel, general, and effective technique for optimization rewriting based on comparator networks which are specific kinds of circuits for reordering the elements of vectors. The idea is to connect an ASP encoding of a comparator network to the literals being optimized and to redistribute the weights of these literals over the structure of the network. The encoding captures information about the weight of an answer set in auxiliary atoms in a structured way that is proven to yield exponential improvements during branch-and-bound optimization on an infinite family of example programs. The used comparator network can be tuned freely, for example, to find the best size for a given benchmark class. Experiments show accelerated optimization performance on several benchmark problems.

scholarly journals Combining Answer Set Programming and domain heuristics for solving hard industrial problems (Application Paper)

2016 ◽  
Vol 16 (5-6) ◽  
pp. 653-669 ◽  
Author(s):  
CARMINE DODARO ◽  
PHILIP GASTEIGER ◽  
NICOLA LEONE ◽  
BENJAMIN MUSITSCH ◽  
FRANCESCO RICCA ◽  
...  
Keyword(s):  
Real World ◽  
Answer Set Programming ◽  
Domain Specific ◽  
Real World Problem ◽  
Real World Applications ◽  
Application Paper ◽  
Problem Instances ◽  
Effective Integration ◽  
Configuration Problem ◽  
Answer Set

AbstractAnswer Set Programming (ASP) is a popular logic programming paradigm that has been applied for solving a variety of complex problems. Among the most challenging real-world applications of ASP are two industrial problems defined by Siemens: the Partner Units Problem (PUP) and the Combined Configuration Problem (CCP). The hardest instances of PUP and CCP are out of reach for state-of-the-art ASP solvers. Experiments show that the performance of ASP solvers could be significantly improved by embedding domain-specific heuristics, but a proper effective integration of such criteria in off-the-shelf ASP implementations is not obvious. In this paper the combination of ASP and domain-specific heuristics is studied with the goal of effectively solving real-world problem instances of PUP and CCP. As a byproduct of this activity, the ASP solverwaspwas extended with an interface that eases embedding new external heuristics in the solver. The evaluation shows that our domain-heuristic-driven ASP solver finds solutions for all the real-world instances of PUP and CCP ever provided by Siemens.

aspeed: Solver scheduling via answer set programming

2014 ◽  
Vol 15 (1) ◽  
pp. 117-142 ◽  
Author(s):  
HOLGER HOOS ◽  
ROLAND KAMINSKI ◽  
MARIUS LINDAUER ◽  
TORSTEN SCHAUB
Keyword(s):  
Optimization Problems ◽  
State Of The Art ◽  
Answer Set Programming ◽  
Data Sets ◽  
Tremendous Progress ◽  
Different Types ◽  
Problem Instances ◽  
Answer Set

AbstractAlthough Boolean Constraint Technology has made tremendous progress over the last decade, the efficacy of state-of-the-art solvers is known to vary considerably across different types of problem instances, and is known to depend strongly on algorithm parameters. This problem was addressed by means of a simple, yet effective approach using handmade, uniform, and unordered schedules of multiple solvers inppfolio, which showed very impressive performance in the 2011 Satisfiability Testing (SAT) Competition. Inspired by this, we take advantage of the modeling and solving capacities of Answer Set Programming (ASP) to automatically determine more refined, that is, nonuniform and ordered solver schedules from the existing benchmarking data. We begin by formulating the determination of such schedules as multi-criteria optimization problems and provide corresponding ASP encodings. The resulting encodings are easily customizable for different settings, and the computation of optimum schedules can mostly be done in the blink of an eye, even when dealing with large runtime data sets stemming from many solvers on hundreds to thousands of instances. Also, the fact that our approach can be customized easily enabled us to swiftly adapt it to generate parallel schedules for multi-processor machines.

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.

scholarly journals Abstract Solvers for Computing Cautious Consequences of ASP programs

2019 ◽  
Vol 19 (5-6) ◽  
pp. 740-756
Author(s):  
GIOVANNI AMENDOLA ◽  
CARMINE DODARO ◽  
MARCO MARATEA
Keyword(s):  
State Of The Art ◽  
Answer Set Programming ◽  
Satisfiability Modulo Theories ◽  
Benchmark Problems ◽  
Propositional Satisfiability ◽  
New Techniques ◽  
The Past ◽  
Boolean Formulas ◽  
Reasoning Tasks ◽  
Answer Set

AbstractAbstract solvers are a method to formally analyze algorithms that have been profitably used for describing, comparing and composing solving techniques in various fields such as Propositional Satisfiability (SAT), Quantified SAT, Satisfiability Modulo Theories, Answer Set Programming (ASP), and Constraint ASP.In this paper, we design, implement and test novel abstract solutions for cautious reasoning tasks in ASP. We show how to improve the current abstract solvers for cautious reasoning in ASP with new techniques borrowed from backbone computation in SAT, in order to design new solving algorithms. By doing so, 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. We implement some of the new solutions in the ASP solver wasp and show that their performance are comparable to state-of-the-art solutions on the benchmark problems from the past ASP Competitions.

scholarly journals Open answer set programming with guarded programs

2008 ◽  
Vol 9 (4) ◽  
pp. 1-53 ◽  
Author(s):  
Stijn Heymans ◽  
Davy Van Nieuwenborgh ◽  
Dirk Vermeir
Keyword(s):  
Answer Set Programming ◽  
Answer Set

scholarly journals Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming

2013 ◽  
Vol 29 (18) ◽  
pp. 2320-2326 ◽  
Author(s):  
Carito Guziolowski ◽  
Santiago Videla ◽  
Federica Eduati ◽  
Sven Thiele ◽  
Thomas Cokelaer ◽  
...  
Keyword(s):  
Answer Set Programming ◽  
Signaling Network ◽  
Logic Models ◽  
Answer Set

CoreALMlib: An library translated from the Component Library

2016 ◽  
Vol 16 (5-6) ◽  
pp. 800-816 ◽  
Author(s):  
DANIELA INCLEZAN
Keyword(s):  
Natural Language ◽  
Answer Set Programming ◽  
Natural Language Understanding ◽  
Inference Engine ◽  
Lexical Resources ◽  
Language Understanding ◽  
Commonsense Knowledge ◽  
Component Library ◽  
Action Language ◽  
Answer Set

AbstractThis paper presents CoreALMlib, an $\mathscr{ALM}$ library of commonsense knowledge about dynamic domains. The library was obtained by translating part of the Component Library (CLib) into the modular action language $\mathscr{ALM}$. CLib consists of general reusable and composable commonsense concepts, selected based on a thorough study of ontological and lexical resources. Our translation targets CLibstates (i.e., fluents) and actions. The resulting $\mathscr{ALM}$ library contains the descriptions of 123 action classes grouped into 43 reusable modules that are organized into a hierarchy. It is made available online and of interest to researchers in the action language, answer-set programming, and natural language understanding communities. We believe that our translation has two main advantages over its CLib counterpart: (i) it specifies axioms about actions in a more elaboration tolerant and readable way, and (ii) it can be seamlessly integrated with ASP reasoning algorithms (e.g., for planning and postdiction). In contrast, axioms are described in CLib using STRIPS-like operators, and CLib's inference engine cannot handle planning nor postdiction.

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