74,324 research outputs found
Interval Slopes as Numerical Abstract Domain for Floating-Point Variables
The design of embedded control systems is mainly done with model-based tools
such as Matlab/Simulink. Numerical simulation is the central technique of
development and verification of such tools. Floating-point arithmetic, that is
well-known to only provide approximated results, is omnipresent in this
activity. In order to validate the behaviors of numerical simulations using
abstract interpretation-based static analysis, we present, theoretically and
with experiments, a new partially relational abstract domain dedicated to
floating-point variables. It comes from interval expansion of non-linear
functions using slopes and it is able to mimic all the behaviors of the
floating-point arithmetic. Hence it is adapted to prove the absence of run-time
errors or to analyze the numerical precision of embedded control systems
A model-theoretic interpretation of environmentally-induced superselection
Environmentally-induced superselection or "einselection" has been proposed as
an observer-independent mechanism by which apparently classical systems
"emerge" from physical interactions between degrees of freedom described
completely quantum-mechanically. It is shown that einselection can only
generate classical systems if the "environment" is assumed \textit{a priori} to
be classical; einselection therefore does not provide an observer-independent
mechanism by which classicality can emerge from quantum dynamics. Einselection
is then reformulated in terms of positive operator-valued measures (POVMs)
acting on a global quantum state. It is shown that this re-formulation enables
a natural interpretation of apparently-classical systems as virtual machines
that requires no assumptions beyond those of classical computer science.Comment: 15 pages, 1 figure; minor correction
The Mode of Computing
The Turing Machine is the paradigmatic case of computing machines, but there
are others, such as Artificial Neural Networks, Table Computing,
Relational-Indeterminate Computing and diverse forms of analogical computing,
each of which based on a particular underlying intuition of the phenomenon of
computing. This variety can be captured in terms of system levels,
re-interpreting and generalizing Newell's hierarchy, which includes the
knowledge level at the top and the symbol level immediately below it. In this
re-interpretation the knowledge level consists of human knowledge and the
symbol level is generalized into a new level that here is called The Mode of
Computing. Natural computing performed by the brains of humans and non-human
animals with a developed enough neural system should be understood in terms of
a hierarchy of system levels too. By analogy from standard computing machinery
there must be a system level above the neural circuitry levels and directly
below the knowledge level that is named here The mode of Natural Computing. A
central question for Cognition is the characterization of this mode. The Mode
of Computing provides a novel perspective on the phenomena of computing,
interpreting, the representational and non-representational views of cognition,
and consciousness.Comment: 35 pages, 8 figure
Fine-grained timing using genetic programming
In previous work, we have demonstrated that it is possible to use Genetic Programming to minimise the resource consumption of software, such as its power consumption or execution time. In this paper, we investigate the extent to which Genetic Programming can be used to gain fine-grained control over software timing. We introduce the ideas behind our work, and carry out experimentation to find that Genetic Programming is indeed able to produce software with unusual and desirable timing properties, where it is not obvious how a manual approach could replicate such results. In general, we discover that Genetic Programming is most effective in controlling statistical properties of software rather than precise control over its timing for individual inputs. This control may find useful application in cryptography and embedded systems
Sciduction: Combining Induction, Deduction, and Structure for Verification and Synthesis
Even with impressive advances in automated formal methods, certain problems
in system verification and synthesis remain challenging. Examples include the
verification of quantitative properties of software involving constraints on
timing and energy consumption, and the automatic synthesis of systems from
specifications. The major challenges include environment modeling,
incompleteness in specifications, and the complexity of underlying decision
problems.
This position paper proposes sciduction, an approach to tackle these
challenges by integrating inductive inference, deductive reasoning, and
structure hypotheses. Deductive reasoning, which leads from general rules or
concepts to conclusions about specific problem instances, includes techniques
such as logical inference and constraint solving. Inductive inference, which
generalizes from specific instances to yield a concept, includes algorithmic
learning from examples. Structure hypotheses are used to define the class of
artifacts, such as invariants or program fragments, generated during
verification or synthesis. Sciduction constrains inductive and deductive
reasoning using structure hypotheses, and actively combines inductive and
deductive reasoning: for instance, deductive techniques generate examples for
learning, and inductive reasoning is used to guide the deductive engines.
We illustrate this approach with three applications: (i) timing analysis of
software; (ii) synthesis of loop-free programs, and (iii) controller synthesis
for hybrid systems. Some future applications are also discussed
Generating Property-Directed Potential Invariants By Backward Analysis
This paper addresses the issue of lemma generation in a k-induction-based
formal analysis of transition systems, in the linear real/integer arithmetic
fragment. A backward analysis, powered by quantifier elimination, is used to
output preimages of the negation of the proof objective, viewed as unauthorized
states, or gray states. Two heuristics are proposed to take advantage of this
source of information. First, a thorough exploration of the possible
partitionings of the gray state space discovers new relations between state
variables, representing potential invariants. Second, an inexact exploration
regroups and over-approximates disjoint areas of the gray state space, also to
discover new relations between state variables. k-induction is used to isolate
the invariants and check if they strengthen the proof objective. These
heuristics can be used on the first preimage of the backward exploration, and
each time a new one is output, refining the information on the gray states. In
our context of critical avionics embedded systems, we show that our approach is
able to outperform other academic or commercial tools on examples of interest
in our application field. The method is introduced and motivated through two
main examples, one of which was provided by Rockwell Collins, in a
collaborative formal verification framework.Comment: In Proceedings FTSCS 2012, arXiv:1212.657
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