3,043 research outputs found
On Automated Lemma Generation for Separation Logic with Inductive Definitions
Separation Logic with inductive definitions is a well-known approach for
deductive verification of programs that manipulate dynamic data structures.
Deciding verification conditions in this context is usually based on
user-provided lemmas relating the inductive definitions. We propose a novel
approach for generating these lemmas automatically which is based on simple
syntactic criteria and deterministic strategies for applying them. Our approach
focuses on iterative programs, although it can be applied to recursive programs
as well, and specifications that describe not only the shape of the data
structures, but also their content or their size. Empirically, we find that our
approach is powerful enough to deal with sophisticated benchmarks, e.g.,
iterative procedures for searching, inserting, or deleting elements in sorted
lists, binary search tress, red-black trees, and AVL trees, in a very efficient
way
Limits of Preprocessing
We present a first theoretical analysis of the power of polynomial-time
preprocessing for important combinatorial problems from various areas in AI. We
consider problems from Constraint Satisfaction, Global Constraints,
Satisfiability, Nonmonotonic and Bayesian Reasoning. We show that, subject to a
complexity theoretic assumption, none of the considered problems can be reduced
by polynomial-time preprocessing to a problem kernel whose size is polynomial
in a structural problem parameter of the input, such as induced width or
backdoor size. Our results provide a firm theoretical boundary for the
performance of polynomial-time preprocessing algorithms for the considered
problems.Comment: This is a slightly longer version of a paper that appeared in the
proceedings of AAAI 201
Reducing fuzzy answer set programming to model finding in fuzzy logics
In recent years, answer set programming (ASP) has been extended to deal with multivalued predicates. The resulting formalisms allow for the modeling of continuous problems as elegantly as ASP allows for the modeling of discrete problems, by combining the stable model semantics underlying ASP with fuzzy logics. However, contrary to the case of classical ASP where many efficient solvers have been constructed, to date there is no efficient fuzzy ASP solver. A well-known technique for classical ASP consists of translating an ASP program P to a propositional theory whose models exactly correspond to the answer sets of P. In this paper, we show how this idea can be extended to fuzzy ASP, paving the way to implement efficient fuzzy ASP solvers that can take advantage of existing fuzzy logic reasoners
Loop Formulas for Description Logic Programs
Description Logic Programs (dl-programs) proposed by Eiter et al. constitute
an elegant yet powerful formalism for the integration of answer set programming
with description logics, for the Semantic Web. In this paper, we generalize the
notions of completion and loop formulas of logic programs to description logic
programs and show that the answer sets of a dl-program can be precisely
captured by the models of its completion and loop formulas. Furthermore, we
propose a new, alternative semantics for dl-programs, called the {\em canonical
answer set semantics}, which is defined by the models of completion that
satisfy what are called canonical loop formulas. A desirable property of
canonical answer sets is that they are free of circular justifications. Some
properties of canonical answer sets are also explored.Comment: 29 pages, 1 figures (in pdf), a short version appeared in ICLP'1
Bounded LTL Model Checking with Stable Models
In this paper bounded model checking of asynchronous concurrent systems is
introduced as a promising application area for answer set programming. As the
model of asynchronous systems a generalisation of communicating automata,
1-safe Petri nets, are used. It is shown how a 1-safe Petri net and a
requirement on the behaviour of the net can be translated into a logic program
such that the bounded model checking problem for the net can be solved by
computing stable models of the corresponding program. The use of the stable
model semantics leads to compact encodings of bounded reachability and deadlock
detection tasks as well as the more general problem of bounded model checking
of linear temporal logic. Correctness proofs of the devised translations are
given, and some experimental results using the translation and the Smodels
system are presented.Comment: 32 pages, to appear in Theory and Practice of Logic Programmin
Properties and Applications of Programs with Monotone and Convex Constraints
We study properties of programs with monotone and convex constraints. We
extend to these formalisms concepts and results from normal logic programming.
They include the notions of strong and uniform equivalence with their
characterizations, tight programs and Fages Lemma, program completion and loop
formulas. Our results provide an abstract account of properties of some recent
extensions of logic programming with aggregates, especially the formalism of
lparse programs. They imply a method to compute stable models of lparse
programs by means of off-the-shelf solvers of pseudo-boolean constraints, which
is often much faster than the smodels system
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