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