1 research outputs found
Optimizing Answer Set Computation via Heuristic-Based Decomposition
Answer Set Programming (ASP) is a purely declarative formalism developed in
the field of logic programming and nonmonotonic reasoning: computational
problems are encoded by logic programs whose answer sets, corresponding to
solutions, are computed by an ASP system. Different, semantically equivalent,
programs can be defined for the same problem; however, performance of systems
evaluating them might significantly vary. We propose an approach for
automatically transforming an input logic program into an equivalent one that
can be evaluated more efficiently. One can make use of existing
tree-decomposition techniques for rewriting selected rules into a set of
multiple ones; the idea is to guide and adaptively apply them on the basis of
proper new heuristics, to obtain a smart rewriting algorithm to be integrated
into an ASP system. The method is rather general: it can be adapted to any
system and implement different preference policies. Furthermore, we define a
set of new heuristics tailored at optimizing grounding, one of the main phases
of the ASP computation; we use them in order to implement the approach into the
ASP system DLV, in particular into its grounding subsystem I-DLV, and carry out
an extensive experimental activity for assessing the impact of the proposal.
Under consideration in Theory and Practice of Logic Programming (TPLP).Comment: 28 pages, 6 figures, 4 tables, BEST PAPER AWARD at PADL 2018 (Los
Angeles, CA, USA), Under consideration in Theory and Practice of Logic
Programming (TPLP