14,702 research outputs found
TOR: modular search with hookable disjunction
Horn Clause Programs have a natural exhaustive depth-first procedural
semantics. However, for many programs this semantics is
ineffective. In order to compute useful solutions, one needs the
ability to modify the search method that explores the alternative
execution branches.
Tor, a well-defined hook into Prolog disjunction, provides this ability.
It is light-weight thanks to its library approach and efficient
because it is based on program transformation.
Tor is general enough to mimic search-modifying
predicates like ECLiPSe's search/6. Moreover, Tor supports
modular composition of search methods and other hooks.
The Tor library is already
provided and used as an add-on to SWI-Prolog.publisher: Elsevier
articletitle: Tor: Modular search with hookable disjunction
journaltitle: Science of Computer Programming
articlelink: http://dx.doi.org/10.1016/j.scico.2013.05.008
content_type: article
copyright: Copyright © 2013 Elsevier B.V. All rights reserved.status: publishe
A linear programming based heuristic framework for min-max regret combinatorial optimization problems with interval costs
This work deals with a class of problems under interval data uncertainty,
namely interval robust-hard problems, composed of interval data min-max regret
generalizations of classical NP-hard combinatorial problems modeled as 0-1
integer linear programming problems. These problems are more challenging than
other interval data min-max regret problems, as solely computing the cost of
any feasible solution requires solving an instance of an NP-hard problem. The
state-of-the-art exact algorithms in the literature are based on the generation
of a possibly exponential number of cuts. As each cut separation involves the
resolution of an NP-hard classical optimization problem, the size of the
instances that can be solved efficiently is relatively small. To smooth this
issue, we present a modeling technique for interval robust-hard problems in the
context of a heuristic framework. The heuristic obtains feasible solutions by
exploring dual information of a linearly relaxed model associated with the
classical optimization problem counterpart. Computational experiments for
interval data min-max regret versions of the restricted shortest path problem
and the set covering problem show that our heuristic is able to find optimal or
near-optimal solutions and also improves the primal bounds obtained by a
state-of-the-art exact algorithm and a 2-approximation procedure for interval
data min-max regret problems
A General Framework for Automatic Termination Analysis of Logic Programs
This paper describes a general framework for automatic termination analysis
of logic programs, where we understand by ``termination'' the finitenes s of
the LD-tree constructed for the program and a given query. A general property
of mappings from a certain subset of the branches of an infinite LD-tree into a
finite set is proved. From this result several termination theorems are
derived, by using different finite sets. The first two are formulated for the
predicate dependency and atom dependency graphs. Then a general result for the
case of the query-mapping pairs relevant to a program is proved (cf.
\cite{Sagiv,Lindenstrauss:Sagiv}). The correctness of the {\em TermiLog} system
described in \cite{Lindenstrauss:Sagiv:Serebrenik} follows from it. In this
system it is not possible to prove termination for programs involving
arithmetic predicates, since the usual order for the integers is not
well-founded. A new method, which can be easily incorporated in {\em TermiLog}
or similar systems, is presented, which makes it possible to prove termination
for programs involving arithmetic predicates. It is based on combining a finite
abstraction of the integers with the technique of the query-mapping pairs, and
is essentially capable of dividing a termination proof into several cases, such
that a simple termination function suffices for each case. Finally several
possible extensions are outlined
Anytime Computation of Cautious Consequences in Answer Set Programming
Query answering in Answer Set Programming (ASP) is usually solved by
computing (a subset of) the cautious consequences of a logic program. This task
is computationally very hard, and there are programs for which computing
cautious consequences is not viable in reasonable time. However, current ASP
solvers produce the (whole) set of cautious consequences only at the end of
their computation. This paper reports on strategies for computing cautious
consequences, also introducing anytime algorithms able to produce sound answers
during the computation.Comment: To appear in Theory and Practice of Logic Programmin
On the Implementation of the Probabilistic Logic Programming Language ProbLog
The past few years have seen a surge of interest in the field of
probabilistic logic learning and statistical relational learning. In this
endeavor, many probabilistic logics have been developed. ProbLog is a recent
probabilistic extension of Prolog motivated by the mining of large biological
networks. In ProbLog, facts can be labeled with probabilities. These facts are
treated as mutually independent random variables that indicate whether these
facts belong to a randomly sampled program. Different kinds of queries can be
posed to ProbLog programs. We introduce algorithms that allow the efficient
execution of these queries, discuss their implementation on top of the
YAP-Prolog system, and evaluate their performance in the context of large
networks of biological entities.Comment: 28 pages; To appear in Theory and Practice of Logic Programming
(TPLP
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