Overall Objectives
New Software and Platforms
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Bibliography
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Section: Research Program

The DREAM team is investigating declarative approaches to solve complex problems such as causal reasoning, landscape simultation and pattern mining. One such approach is ASP.

ASP (Answer set programming) [43] , [31] is an approach to declarative problem solving, combining a rich yet simple modelling language with high-performance solving capacities, tailored to Knowledge Representation and Reasoning. "Declarative problem solving" means that the program is close to the way a problem is enunciated, and not to the way the problem is solved. This facilitates writing and revising programs. ASP is an outgrowth of research on the use of non monotonic reasoning in knowledge representation. ASP programs[23] consist in rules that look like Prolog rules, but the computational mechanism is different [54] .

ASP allows to solve search problems in NP (and theoretically in NP${}^{NP}$) in a uniform way (being more compact than boolean approaches like SAT solvers). ASP is good when dealing with knowledge representation, particularly when logical rules or graphs are involved. The versatility of ASP is reflected by the ASP solver clasp, winning first places at ASP, SAT and other competitions.

ASP solvers deal with propositional rules, however in practice predicates are allowed. A grounder replaces each free variable of the program provided by users with any eligible constant symbol. The output of the grounder is thus a propositional program, which is piped into a solver which then computes answer sets. These answer sets are the models for the ASP theory, and they constitute the result of an ASP program. The user may ask for all the models, or only one, or any number $n$ of models. The most powerful version (clingo, which combines the grounder gringo and the solver clasp) is from Torsten Schaub's team (see http://potassco.sourceforge.net for the last version of clingo, including a guide). These versions can be easily interfaced with python programs, which extends further the practical applicability of ASP [42] .

The main interests of using ASP are: 1) the ease to write and to update programs, and 2) the efficiency of the ASP solvers (improved in the recent versions).

Our main challenge is to propose ASP modeling that scales up to solving real problems. We are especially working on the modeling of sequential pattern mining with ASP in order to mine real datasets in a flexible and efficient way.

Our second challenge is to model a wide range of expert knowledge to include reasoning into the solving processes, in order to output more meaningful results.