36 research outputs found
Logic Programming Approaches for Representing and Solving Constraint Satisfaction Problems: A Comparison
Many logic programming based approaches can be used to describe and solve
combinatorial search problems. On the one hand there is constraint logic
programming which computes a solution as an answer substitution to a query
containing the variables of the constraint satisfaction problem. On the other
hand there are systems based on stable model semantics, abductive systems, and
first order logic model generators which compute solutions as models of some
theory. This paper compares these different approaches from the point of view
of knowledge representation (how declarative are the programs) and from the
point of view of performance (how good are they at solving typical problems).Comment: 15 pages, 3 eps-figure
Recycling Computed Answers in Rewrite Systems for Abduction
In rule-based systems, goal-oriented computations correspond naturally to the
possible ways that an observation may be explained. In some applications, we
need to compute explanations for a series of observations with the same domain.
The question whether previously computed answers can be recycled arises. A yes
answer could result in substantial savings of repeated computations. For
systems based on classic logic, the answer is YES. For nonmonotonic systems
however, one tends to believe that the answer should be NO, since recycling is
a form of adding information. In this paper, we show that computed answers can
always be recycled, in a nontrivial way, for the class of rewrite procedures
that we proposed earlier for logic programs with negation. We present some
experimental results on an encoding of the logistics domain.Comment: 20 pages. Full version of our IJCAI-03 pape
Tensor-based abduction in horn propositional programs
This paper proposes an algorithm for computing solutions of abductive Horn propositional tasks using third-order tensors. We first introduce the notion of explanatory operator, a single-step operation based on inverted implication, and prove that minimal abductive solutions of a given Horn propositional task can be correctly computed using this operator. We then provide a mapping of Horn propositional programs into third-order tensors, which builds upon recent work on matrix representation of Horn programs. We finally show how this mapping can be used to compute the explanatory operator by tensor multiplication
Hypothetical reasoning with well founded semantics
publishersversionpublishe
Applications of Intuitionistic Logic in Answer Set Programming
We present some applications of intermediate logics in the field of Answer
Set Programming (ASP). A brief, but comprehensive introduction to the answer
set semantics, intuitionistic and other intermediate logics is given. Some
equivalence notions and their applications are discussed. Some results on
intermediate logics are shown, and applied later to prove properties of answer
sets. A characterization of answer sets for logic programs with nested
expressions is provided in terms of intuitionistic provability, generalizing a
recent result given by Pearce.
It is known that the answer set semantics for logic programs with nested
expressions may select non-minimal models. Minimal models can be very important
in some applications, therefore we studied them; in particular we obtain a
characterization, in terms of intuitionistic logic, of answer sets which are
also minimal models. We show that the logic G3 characterizes the notion of
strong equivalence between programs under the semantic induced by these models.
Finally we discuss possible applications and consequences of our results. They
clearly state interesting links between ASP and intermediate logics, which
might bring research in these two areas together.Comment: 30 pages, Under consideration for publication in Theory and Practice
of Logic Programmin
Context interchange : new features and formalisms for the intelligent integration of information
Cover title.Includes bibliographical references (p. 22-24).Supported in part by the National Financial Services Research Center (IFSRC), the PROductivity From Information Technology (PROFIT) project at MIT, ARPA, and UASF/Rome Laboratory. F30602-93-C-0160Cheng Hian Goh ... [et al.]