17,148 research outputs found
Applying machine learning to the problem of choosing a heuristic to select the variable ordering for cylindrical algebraic decomposition
Cylindrical algebraic decomposition(CAD) is a key tool in computational
algebraic geometry, particularly for quantifier elimination over real-closed
fields. When using CAD, there is often a choice for the ordering placed on the
variables. This can be important, with some problems infeasible with one
variable ordering but easy with another. Machine learning is the process of
fitting a computer model to a complex function based on properties learned from
measured data. In this paper we use machine learning (specifically a support
vector machine) to select between heuristics for choosing a variable ordering,
outperforming each of the separate heuristics.Comment: 16 page
Synthesizing Short-Circuiting Validation of Data Structure Invariants
This paper presents incremental verification-validation, a novel approach for
checking rich data structure invariants expressed as separation logic
assertions. Incremental verification-validation combines static verification of
separation properties with efficient, short-circuiting dynamic validation of
arbitrarily rich data constraints. A data structure invariant checker is an
inductive predicate in separation logic with an executable interpretation; a
short-circuiting checker is an invariant checker that stops checking whenever
it detects at run time that an assertion for some sub-structure has been fully
proven statically. At a high level, our approach does two things: it statically
proves the separation properties of data structure invariants using a static
shape analysis in a standard way but then leverages this proof in a novel
manner to synthesize short-circuiting dynamic validation of the data
properties. As a consequence, we enable dynamic validation to make up for
imprecision in sound static analysis while simultaneously leveraging the static
verification to make the remaining dynamic validation efficient. We show
empirically that short-circuiting can yield asymptotic improvements in dynamic
validation, with low overhead over no validation, even in cases where static
verification is incomplete
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Silicon compilation
Silicon compilation is a term used for many different purposes. In this paper we define silicon compilation as a mapping from some higher level description into layout. We define the basic issues in structural and behavioral silicon compilation and some possible solutions to those issues. Finally, we define the concept of an intelligent silicon compiler in which the compiler evaluates the quality of the generated design and attempts to improve it if it is not satisfactory
Natural Abduction: the Bridge between Individuals Choices and the Production of Evolutionary Innovations
Defenders of developmental systems theory have shown the need for a new evolutionary synthesis. However this effort requires a metaphysical evolutionary framework in which such an expanded synthesis can be embedded. I propose to develop a Peircean monist ontology by explaining the six characteristics or facets of reality labeled as: A) spontaneity, B) determinism, C) processing of information, D) statistical law, E) internally driven capricious choices and F) tendency to form habits. A special emphasis will be dedicated to characterize the predicates C and E as they provide the clue to the understanding of nature as a continuously open-ended and unpredictable process, where its inherent creativity is analogized to an abductive inference
Safety-Aware Apprenticeship Learning
Apprenticeship learning (AL) is a kind of Learning from Demonstration
techniques where the reward function of a Markov Decision Process (MDP) is
unknown to the learning agent and the agent has to derive a good policy by
observing an expert's demonstrations. In this paper, we study the problem of
how to make AL algorithms inherently safe while still meeting its learning
objective. We consider a setting where the unknown reward function is assumed
to be a linear combination of a set of state features, and the safety property
is specified in Probabilistic Computation Tree Logic (PCTL). By embedding
probabilistic model checking inside AL, we propose a novel
counterexample-guided approach that can ensure safety while retaining
performance of the learnt policy. We demonstrate the effectiveness of our
approach on several challenging AL scenarios where safety is essential.Comment: Accepted by International Conference on Computer Aided Verification
(CAV) 201
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