3,172 research outputs found
Efficient Generation of Craig Interpolants in Satisfiability Modulo Theories
The problem of computing Craig Interpolants has recently received a lot of
interest. In this paper, we address the problem of efficient generation of
interpolants for some important fragments of first order logic, which are
amenable for effective decision procedures, called Satisfiability Modulo Theory
solvers.
We make the following contributions.
First, we provide interpolation procedures for several basic theories of
interest: the theories of linear arithmetic over the rationals, difference
logic over rationals and integers, and UTVPI over rationals and integers.
Second, we define a novel approach to interpolate combinations of theories,
that applies to the Delayed Theory Combination approach.
Efficiency is ensured by the fact that the proposed interpolation algorithms
extend state of the art algorithms for Satisfiability Modulo Theories. Our
experimental evaluation shows that the MathSAT SMT solver can produce
interpolants with minor overhead in search, and much more efficiently than
other competitor solvers.Comment: submitted to ACM Transactions on Computational Logic (TOCL
Proving Finite Satisfiability of Deductive Databases
It is shown how certain refutation methods can be extended into semi-decision
procedures that are complete for both unsatisfiability and finite satisfiability. The proposed extension
is justified by a new characterization of finite satisfiability. This research was motivated
by a database design problem: Deduction rules and integrity constraints in definite databases
have to be finitely satisfiabl
A New General Method to Generate Random Modal Formulae for Testing Decision Procedures
The recent emergence of heavily-optimized modal decision procedures has highlighted the key role of empirical testing in this domain. Unfortunately, the introduction of extensive empirical tests for modal logics is recent, and so far none of the proposed test generators is very satisfactory. To cope with this fact, we present a new random generation method that provides benefits over previous methods for generating empirical tests. It fixes and much generalizes one of the best-known methods, the random CNF_[]m test, allowing for generating a much wider variety of problems, covering in principle the whole input space. Our new method produces much more suitable test sets for the current generation of modal decision procedures. We analyze the features of the new method by means of an extensive collection of empirical tests
On Improving Local Search for Unsatisfiability
Stochastic local search (SLS) has been an active field of research in the
last few years, with new techniques and procedures being developed at an
astonishing rate. SLS has been traditionally associated with satisfiability
solving, that is, finding a solution for a given problem instance, as its
intrinsic nature does not address unsatisfiable problems. Unsatisfiable
instances were therefore commonly solved using backtrack search solvers. For
this reason, in the late 90s Selman, Kautz and McAllester proposed a challenge
to use local search instead to prove unsatisfiability. More recently, two SLS
solvers - Ranger and Gunsat - have been developed, which are able to prove
unsatisfiability albeit being SLS solvers. In this paper, we first compare
Ranger with Gunsat and then propose to improve Ranger performance using some of
Gunsat's techniques, namely unit propagation look-ahead and extended
resolution
A New General Method to Generate Random Modal Formulae for Testing Decision Procedures
The recent emergence of heavily-optimized modal decision procedures has
highlighted the key role of empirical testing in this domain. Unfortunately,
the introduction of extensive empirical tests for modal logics is recent, and
so far none of the proposed test generators is very satisfactory. To cope with
this fact, we present a new random generation method that provides benefits
over previous methods for generating empirical tests. It fixes and much
generalizes one of the best-known methods, the random CNF_[]m test, allowing
for generating a much wider variety of problems, covering in principle the
whole input space. Our new method produces much more suitable test sets for the
current generation of modal decision procedures. We analyze the features of the
new method by means of an extensive collection of empirical tests
Synthesizing Multiple Boolean Functions using Interpolation on a Single Proof
It is often difficult to correctly implement a Boolean controller for a
complex system, especially when concurrency is involved. Yet, it may be easy to
formally specify a controller. For instance, for a pipelined processor it
suffices to state that the visible behavior of the pipelined system should be
identical to a non-pipelined reference system (Burch-Dill paradigm). We present
a novel procedure to efficiently synthesize multiple Boolean control signals
from a specification given as a quantified first-order formula (with a specific
quantifier structure). Our approach uses uninterpreted functions to abstract
details of the design. We construct an unsatisfiable SMT formula from the given
specification. Then, from just one proof of unsatisfiability, we use a variant
of Craig interpolation to compute multiple coordinated interpolants that
implement the Boolean control signals. Our method avoids iterative learning and
back-substitution of the control functions. We applied our approach to
synthesize a controller for a simple two-stage pipelined processor, and present
first experimental results.Comment: This paper originally appeared in FMCAD 2013,
http://www.cs.utexas.edu/users/hunt/FMCAD/FMCAD13/index.shtml. This version
includes an appendix that is missing in the conference versio
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