19 research outputs found
The Pyglaf Argumentation Reasoner
The pyglaf reasoner takes advantage of circumscription to solve computational problems of abstract argumentation frameworks.
In fact, many of these problems are reduced to circumscription by means of linear encodings, and a few others are solved by means of a sequence of calls to an oracle for circumscription.
Within pyglaf, Python is used to build the encodings and to control the execution of the external circumscription solver, which extends the SAT solver glucose and implements an algorithm based on unsatisfiable core analysis
Propagators and Solvers for the Algebra of Modular Systems
To appear in the proceedings of LPAR 21.
Solving complex problems can involve non-trivial combinations of distinct
knowledge bases and problem solvers. The Algebra of Modular Systems is a
knowledge representation framework that provides a method for formally
specifying such systems in purely semantic terms. Formally, an expression of
the algebra defines a class of structures. Many expressive formalism used in
practice solve the model expansion task, where a structure is given on the
input and an expansion of this structure in the defined class of structures is
searched (this practice overcomes the common undecidability problem for
expressive logics). In this paper, we construct a solver for the model
expansion task for a complex modular systems from an expression in the algebra
and black-box propagators or solvers for the primitive modules. To this end, we
define a general notion of propagators equipped with an explanation mechanism,
an extension of the alge- bra to propagators, and a lazy conflict-driven
learning algorithm. The result is a framework for seamlessly combining solving
technology from different domains to produce a solver for a combined system.Comment: To appear in the proceedings of LPAR 2
On the Configuration of More and Less Expressive Logic Programs
The decoupling between the representation of a certain problem, i.e., its
knowledge model, and the reasoning side is one of main strong points of
model-based Artificial Intelligence (AI). This allows, e.g. to focus on
improving the reasoning side by having advantages on the whole solving process.
Further, it is also well-known that many solvers are very sensitive to even
syntactic changes in the input. In this paper, we focus on improving the
reasoning side by taking advantages of such sensitivity. We consider two
well-known model-based AI methodologies, SAT and ASP, define a number of
syntactic features that may characterise their inputs, and use automated
configuration tools to reformulate the input formula or program. Results of a
wide experimental analysis involving SAT and ASP domains, taken from respective
competitions, show the different advantages that can be obtained by using input
reformulation and configuration. Under consideration in Theory and Practice of
Logic Programming (TPLP).Comment: Under consideration in Theory and Practice of Logic Programming
(TPLP