Constraints, lazy constraints, or propagators in ASP solving: An empirical analysis*

Answer Set Programming (ASP) is a well-established declarative paradigm. One of the successes of ASP is the availability of efficient systems. State-of-the-art systems are based on the ground+solve approach. In some applications this approach is infeasible because the grounding of one or few constraints is expensive. In this paper, we systematically compare alternative strategies to avoid the instantiation of problematic constraints, that are based on custom extensions of the solver. Results on real and synthetic benchmarks highlight some strengths and weaknesses of the different strategies. (Under consideration for acceptance in TPLP, ICLP 2017 Special Issue.)

[1]  Christoph Redl,et al.  The dlvhex system for knowledge representation: recent advances (system description)* , 2016, Theory and Practice of Logic Programming.

[2]  Peter Schüller,et al.  A systematic analysis of levels of integration between high-level task planning and low-level feasibility checks , 2016, AI Commun..

[3]  J. P. Marques,et al.  GRASP : A Search Algorithm for Propositional Satisfiability , 1999 .

[4]  Peter Schüller,et al.  External Propagators in WASP: Preliminary Report , 2016, RCRA@AI*IA.

[5]  Georg Gottlob,et al.  Optimization Methods for the Partner Units Problem , 2011, CPAIOR.

[6]  Antonius Weinzierl,et al.  Blending Lazy-Grounding and CDNL Search for Answer-Set Solving , 2017, LPNMR.

[7]  Miroslaw Truszczynski,et al.  Logic programs with abstract constraint atoms: The role of computations , 2007, Artif. Intell..

[8]  Peter Schüller.  Modeling Variations of First-Order Horn Abduction in Answer Set Programming , 2016, Fundam. Informaticae.

[9]  Michael Gelfond,et al.  An A Prolog decision support system for the Space Shuttle , 2001, Answer Set Programming.

[10]  Yuliya Lierler,et al.  SMT-Based Constraint Answer Set Solver EZSMT (System Description) , 2016, ICLP.

[11]  Michael Gelfond,et al.  Classical negation in logic programs and disjunctive databases , 1991, New Generation Computing.

[12]  Stefan Woltran,et al.  The power of non-ground rules in Answer Set Programming , 2016, Theory and Practice of Logic Programming.

[13]  Yuliya Lierler,et al.  Integration Schemas for Constraint Answer Set Programming: a Case Study , 2013, Theory Pract. Log. Program..

[14]  Antonius Weinzierl,et al.  OMiGA : An Open Minded Grounding On-The-Fly Answer Set Solver , 2012, JELIA.

[15]  Martin Gebser,et al.  Domain-Specific Heuristics in Answer Set Programming , 2013, AAAI.

[16]  Peter J. Stuckey,et al.  Lazy Model Expansion: Interleaving Grounding with Search , 2014, J. Artif. Intell. Res..

[17]  Pascal Nicolas,et al.  The First Version of a New ASP Solver : ASPeRiX , 2009, LPNMR.

[18]  Esra Erdem,et al.  Under Consideration for Publication in Theory and Practice of Logic Programming Generating Explanations for Biomedical Queries , 2022 .

[19]  Peter J. Stuckey,et al.  Stable model semantics for founded bounds , 2013, Theory Pract. Log. Program..

[20]  Alessandro Dal Palù,et al.  GASP: Answer Set Programming with Lazy Grounding , 2009, Fundam. Informaticae.

[21]  Martin Gebser,et al.  Theory Solving Made Easy with Clingo 5 , 2016, ICLP.

[22]  Michael Gelfond,et al.  A Preliminary Report on Integrating of Answer Set and Constraint Solving , 2005, Answer Set Programming.

[23]  Thomas Eiter,et al.  A model building framework for answer set programming with external computations* , 2015, Theory and Practice of Logic Programming.

[24]  Dimitris Achlioptas,et al.  Random Satisfiability , 2009, Handbook of Satisfiability.

[25]  Martin Gebser,et al.  Design and results of the Fifth Answer Set Programming Competition , 2016, Artif. Intell..

[26]  Miroslaw Truszczynski,et al.  Answer set programming at a glance , 2011, Commun. ACM.

[27]  Torsten Schaub,et al.  ASP modulo CSP: The clingcon system , 2012, Theory and Practice of Logic Programming.

[28]  Mario Alviano,et al.  Completion of Disjunctive Logic Programs , 2016, IJCAI.

[29]  Mario Alviano,et al.  Advances in WASP , 2015, LPNMR.

[30]  Thomas Eiter,et al.  Problem Solving Using the HEX Family , 2016, Computational Models of Rationality.

[31]  Tomi Männistö,et al.  Towards Intelligent Support for Managing Evolution of Configurable Software Product Families , 2003, SCM.

[32]  Tomi Janhunen,et al.  Optimizing phylogenetic supertrees using answer set programming , 2015, Theory and Practice of Logic Programming.

[33]  Christoph Beierle,et al.  Computational Models of Rationality, Essays dedicated to Gabriele Kern-Isberner on the occasion of her 60th birthday , 2016, Computational Models of Rationality.

[34]  Toby Walsh,et al.  Handbook of satisfiability , 2009 .

[35]  Yuliya Lierler,et al.  Constraint answer set solver EZCSP and why integration schemas matter , 2017, Theory and Practice of Logic Programming.

[36]  Peter J. Stuckey,et al.  Lazy Clause Generation Reengineered , 2009, CP.

[37]  Francesco Ricca,et al.  Combining Answer Set Programming and domain heuristics for solving hard industrial problems (Application Paper) , 2016, Theory and Practice of Logic Programming.

[38]  Giorgio Terracina,et al.  Taming primary key violations to query large inconsistent data via ASP , 2015, Theory and Practice of Logic Programming.

[39]  Torsten Schaub,et al.  Grounding and Solving in Answer Set Programming , 2016, AI Mag..