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 among 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

MASP-Reduce: A Proposal for Distributed Computation of Stable Models

There has been an increasing interest in recent years towards the development of efficient solvers for Answer Set Programming (ASP) and towards the application of ASP to solve increasing more challenging problems. In particular, several recent efforts have explored the issue of scalability of ASP solvers when addressing the challenges caused by the need to ground the program before resolution. This paper offers an alternative solution to this challenge, focused on the use of distributed programming techniques to reason about ASP programs whose grounding would be prohibitive for mainstream ASP solvers. The work builds on a proposal of a characterization of answer set solving as a form of non-standard graph coloring. The paper expands this characterization to include syntactic extensions used in modern ASP (e.g., choice rules, weight constraints). We present an implementation of the solver using a distributed programming framework specifically designed to manipulate very large graphs, as provided by Apache Spark, which in turn builds on the MapReduce programming framework. Finally, we provide a few preliminary results obtained from the first prototype implementation of this approach

Speeding up Lazy-Grounding Answer Set Solving

The grounding bottleneck is an important open issue in Answer Set Programming. Lazy grounding addresses it by interleaving grounding and search. The performance of current lazy-grounding solvers is not yet comparable to that of ground-and-solve systems, however. The aim of this thesis is to extend prior work on lazy grounding by novel heuristics and other techniques like non-ground conflict learning in order to speed up solving. Parts of expected results will be beneficial for ground-and-solve systems as well

Computing Query Answering With Non-Monotonic Rules: A Case Study of Archaeology Qualitative Spatial Reasoning

International audienceThis paper deals with querying ontology-based knowledge bases equipped with non-monotonic rules through a case study within the framework of Cultural Heritage. It focuses on 3D underwater surveys on the Xlendi wreck which is represented by an OWL2 knowledge base with a large dataset. The paper aims at improving the interactions between the archaeologists and the knowledge base providing new queries that involve non-monotonic rules in order to perform qualitative spatial reasoning. To this end, the knowledge base initially represented in OWL2-QL is translated into an equivalent Answer Set Programming (ASP) program and is enriched with a set of non-monotonic ASP rules suitable to express default and exceptions. An ASP query answering approach is proposed and implemented. Furthermore due to the increased expressiveness of non-monotonic rules it provides spatial reasoning and spatial relations between artifacts query answering which is not possible with query answering languages such as SPARQL and SQWRL