974 research outputs found
CHR Grammars
A grammar formalism based upon CHR is proposed analogously to the way
Definite Clause Grammars are defined and implemented on top of Prolog. These
grammars execute as robust bottom-up parsers with an inherent treatment of
ambiguity and a high flexibility to model various linguistic phenomena. The
formalism extends previous logic programming based grammars with a form of
context-sensitive rules and the possibility to include extra-grammatical
hypotheses in both head and body of grammar rules. Among the applications are
straightforward implementations of Assumption Grammars and abduction under
integrity constraints for language analysis. CHR grammars appear as a powerful
tool for specification and implementation of language processors and may be
proposed as a new standard for bottom-up grammars in logic programming.
To appear in Theory and Practice of Logic Programming (TPLP), 2005Comment: 36 pp. To appear in TPLP, 200
A Survey of Symbolic Execution Techniques
Many security and software testing applications require checking whether
certain properties of a program hold for any possible usage scenario. For
instance, a tool for identifying software vulnerabilities may need to rule out
the existence of any backdoor to bypass a program's authentication. One
approach would be to test the program using different, possibly random inputs.
As the backdoor may only be hit for very specific program workloads, automated
exploration of the space of possible inputs is of the essence. Symbolic
execution provides an elegant solution to the problem, by systematically
exploring many possible execution paths at the same time without necessarily
requiring concrete inputs. Rather than taking on fully specified input values,
the technique abstractly represents them as symbols, resorting to constraint
solvers to construct actual instances that would cause property violations.
Symbolic execution has been incubated in dozens of tools developed over the
last four decades, leading to major practical breakthroughs in a number of
prominent software reliability applications. The goal of this survey is to
provide an overview of the main ideas, challenges, and solutions developed in
the area, distilling them for a broad audience.
The present survey has been accepted for publication at ACM Computing
Surveys. If you are considering citing this survey, we would appreciate if you
could use the following BibTeX entry: http://goo.gl/Hf5FvcComment: This is the authors pre-print copy. If you are considering citing
this survey, we would appreciate if you could use the following BibTeX entry:
http://goo.gl/Hf5Fv
Expressive Policy Analysis with Enhanced System Dynamicity
Despite several research studies, the effective analysis of policy based systems remains a significant challenge. Policy analysis should at least (i) be expressive (ii) take account of obligations and authorizations, (iii) include a dynamic system model, and (iv) give useful diagnostic information. We present a logic-based policy analysis framework which satisfies these requirements, showing how many significant policy-related properties can be analysed, and we give details of a prototype implementation. Copyright 2009 ACM
Predicting the approximate functional behaviour of physical systems
This dissertation addresses the problem of the computer prediction of the approximate
behaviour of physical systems describable by ordinary differential equations.Previous approaches to behavioural prediction have either focused on an exact
mathematical description or on a qualitative account. We advocate a middle ground: a
representation more coarse than an exact mathematical solution yet more specific than a
qualitative one. What is required is a mathematical expression, simpler than the exact
solution, whose qualitative features mirror those of the actual solution and whose
functional form captures the principal parameter relationships underlying the behaviour of
the real system. We term such a representation an approximate functional solution.Approximate functional solutions are superior to qualitative descriptions because they
reveal specific functional relationships, restore a quantitative time scale to a process and
support more sophisticated comparative analysis queries. Moreover, they can be superior to
exact mathematical solutions by emphasizing comprehensibility, adequacy and practical
utility over precision.Two strategies for constructing approximate functional solutions are proposed. The first
abstracts the original equation, predicts behaviour in the abstraction space and maps this
back to the approximate functional level. Specifically, analytic abduction exploits
qualitative simulation to predict the qualitative properties of the solution and uses this
knowledge to guide the selection of a parameterized trial function which is then tuned with
respect to the differential equation. In order to limit the complexity of a proposed
approximate functional solution, and hence maintain its comprehensibility,
back-of-the-envelope reasoning is used to simplify overly complex expressions in a
magnitude extreme. If no function is recognised which matches the predicted behaviour,
segment calculus is called upon to find a composite function built from known primitives
and a set of operators. At the very least, segment calculus identifies a plausible structure
for the form of the solution (e.g. that it is a composition of two unknown functions).
Equation parsing capitalizes on this partial information to look for a set of termwise
interactions which, when interpreted, expose a particular solution of the equation.The second, and more direct, strategy for constructing an approximate functional solution is
embodied in the closed form approximation technique. This extends approximation
methods to equations which lack a closed form solution. This involves solving the
differential equation exactly, as an infinite series, and obtaining an approximate functional
solution by constructing a closed form function whose Taylor series is close to that of the
exact solutionThe above techniques dovetail together to achieve a style of reasoning closer to that of an
engineer or physicist rather than a mathematician. The key difference being to sacrifice the
goal of finding the correct solution of the differential equation in favour of finding an
approximation which is adequate for the purpose to which the knowledge will be put.
Applications to Intelligent Tutoring and Design Support Systems are suggested
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