ASPeRiX, a first-order forward chaining approach for answer set computing

The natural way to use Answer Set Programming (ASP) to represent knowledge in Artificial Intelligence or to solve a combinatorial problem is to elaborate a first order logic program with default negation. In a preliminary step this program with variables is translated in an equivalent propositional one by a first tool: the grounder. Then, the propositional program is given to a second tool: the solver. This last one computes (if they exist) one or many answer sets (stable models) of the program, each answer set encoding one solution of the initial problem. Until today, almost all ASP systems apply this two steps computation. In this article, the project ASPeRiX is presented as a first order forward chaining approach for Answer Set Computing. This project was amongst the first to introduce an approach of answer set computing that escapes the preliminary phase of rule instantiation by integrating it in the search process. The methodology applies a forward chaining of first order rules that are grounded on the fly by means of previously produced atoms. Theoretical foundations of the approach are presented, the main algorithms of the ASP solver ASPeRiX are detailed and some experiments and comparisons with existing systems are provided.

[1]  Wolfgang Faber,et al.  The DLV system for knowledge representation and reasoning , 2002, TOCL.

[2]  Mario Alviano,et al.  Function Symbols in ASP : Overview and Perspectives , 2012 .

[3]  Mario Alviano,et al.  Disjunctive ASP with functions: Decidable queries and effective computation , 2010, Theory Pract. Log. Program..

[4]  Miroslaw Truszczynski,et al.  Pbmodels - Software to Compute Stable Models by Pseudoboolean Solvers , 2005, LPNMR.

[5]  Mario Alviano,et al.  WASP: A Native ASP Solver Based on Constraint Learning , 2013, LPNMR.

[6]  Yuliya Lierler,et al.  One More Decidable Class of Finitely Ground Programs , 2009, ICLP.

[7]  Pascal Nicolas,et al.  ASPeRiX, a first-order forward chaining approach for answer set computing* , 2009, Theory and Practice of Logic Programming.

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

[9]  Francesco Scarcello,et al.  Enhancing DLV instantiator by backjumping techniques , 2007, Annals of Mathematics and Artificial Intelligence.

[10]  Wolfgang Faber,et al.  Logic Programming and Nonmonotonic Reasoning , 2011, Lecture Notes in Computer Science.

[11]  Martin Gebser,et al.  Engineering an Incremental ASP Solver , 2008, ICLP.

[12]  M De Vos ASP: The future is bright a position paper , 2009, ICLP 2009.

[13]  Wolfgang Faber,et al.  The Intelligent Grounder of DLV , 2012, Correct Reasoning.

[14]  Georg Gottlob,et al.  A Non-Ground Realization of the Stable and Well-Founded Semantics , 1996, Theor. Comput. Sci..

[15]  Timo Soininen,et al.  Extending and implementing the stable model semantics , 2000, Artif. Intell..

[16]  Tran Cao Son,et al.  Logic Programming and Nonmonotonic Reasoning , 2013, Lecture Notes in Computer Science.

[17]  Martin Gebser,et al.  Advances in gringo Series 3 , 2011, LPNMR.

[18]  V. S. Subrahmanian,et al.  Computing Non-Ground Representations of Stable Models , 1997, LPNMR.

[19]  Giovambattista Ianni,et al.  Computable Functions in ASP: Theory and Implementation , 2008, ICLP.

[20]  Marcello Balduccini,et al.  Representing Constraint Satisfaction Problems in Answer Set Programming , 2022 .

[21]  Martin Gebser,et al.  Conflict-driven answer set solving: From theory to practice , 2012, Artif. Intell..

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

[23]  Piero A. Bonatti,et al.  A decidable subclass of finitary programs , 2010, Theory and Practice of Logic Programming.

[24]  Wolfgang Faber,et al.  Pushing Goal Derivation in DLP Computations , 1999, LPNMR.

[25]  Chitta Baral,et al.  Knowledge Representation, Reasoning and Declarative Problem Solving , 2003 .

[26]  Fangzhen Lin,et al.  ASSAT: computing answer sets of a logic program by SAT solvers , 2002, Artif. Intell..

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

[28]  Martin Gebser,et al.  GrinGo : A New Grounder for Answer Set Programming , 2007, LPNMR.

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

[30]  Sergio Greco,et al.  Logic programming with function symbols: Checking termination of bottom-up evaluation through program adornments , 2013, Theory and Practice of Logic Programming.

[31]  Jeffrey D. Ullman,et al.  Principles of Database and Knowledge-Base Systems, Volume II , 1988, Principles of computer science series.

[32]  Giovambattista Ianni,et al.  The third open answer set programming competition , 2012, Theory and Practice of Logic Programming.

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

[34]  Giovambattista Ianni,et al.  Finitely recursive programs: Decidability and bottom-up computation , 2011, AI Commun..

[35]  Yuliya Lierler,et al.  Answer Set Programming Based on Propositional Satisfiability , 2006, Journal of Automated Reasoning.

[36]  Yi Zhou,et al.  From Answer Set Logic Programming to Circumscription via Logic of GK , 2007, IJCAI.

[37]  Francesco Ricca,et al.  Experimenting with parallelism for the instantiation of ASP programs , 2008, J. Algorithms.

[38]  Torsten Schaub,et al.  Graphs and colorings for answer set programming , 2005, Theory and Practice of Logic Programming.

[39]  Miroslaw Truszczynski,et al.  Connecting First-Order ASP and the Logic FO(ID) through Reducts , 2012, Correct Reasoning.

[40]  Michael Gelfond,et al.  Towards an Integration of Answer Set and Constraint Solving , 2005, ICLP.

[41]  Jeffrey D. Ullman,et al.  Principles of database and knowledge-base systems, Vol. I , 1988 .

[42]  Ilkka Niemelä,et al.  Logic programs with stable model semantics as a constraint programming paradigm , 1999, Annals of Mathematics and Artificial Intelligence.

[43]  Joohyung Lee,et al.  A New Perspective on Stable Models , 2007, IJCAI.

[44]  Francesco Buccafurri,et al.  Enhancing Disjunctive Datalog by Constraints , 2000, IEEE Trans. Knowl. Data Eng..

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