20,481 research outputs found
The use of data-mining for the automatic formation of tactics
This paper discusses the usse of data-mining for the automatic formation of tactics. It was presented at the Workshop on Computer-Supported Mathematical Theory Development held at IJCAR in 2004. The aim of this project is to evaluate the applicability of data-mining techniques to the automatic formation of tactics from large corpuses of proofs. We data-mine information from large proof corpuses to find commonly occurring patterns. These patterns are then evolved into tactics using genetic programming techniques
Carnap: an Open Framework for Formal Reasoning in the Browser
This paper presents an overview of Carnap, a free and open framework for the development of formal reasoning applications. Carnapâs design emphasizes flexibility, extensibility, and rapid prototyping. Carnap-based applications are written in Haskell, but can be compiled to JavaScript to run in standard web browsers. This combination of features makes Carnap ideally suited for educational applications, where ease-of-use is crucial for students and adaptability to different teaching strategies and classroom needs is crucial for instructors. The paper describes Carnapâs implementation, along with its current and projected pedagogical applications
Automatic Verification of Transactions on an Object-Oriented Database
In the context of the object-oriented data model, a compiletime approach is given that provides for a significant reduction of the amount of run-time transaction overhead due to integrity constraint checking. The higher-order logic Isabelle theorem prover is used to automatically prove which constraints might, or might not be violated by a given transaction in a manner analogous to the one used by Sheard and Stemple (1989) for the relational data model. A prototype transaction verification tool has been implemented, which automates the semantic mappings and generates proof goals for Isabelle. Test results are discussed to illustrate the effectiveness of our approach
Equations for Hereditary Substitution in Leivant's Predicative System F: A Case Study
This paper presents a case study of formalizing a normalization proof for
Leivant's Predicative System F using the Equations package. Leivant's
Predicative System F is a stratified version of System F, where type
quantification is annotated with kinds representing universe levels. A weaker
variant of this system was studied by Stump & Eades, employing the hereditary
substitution method to show normalization. We improve on this result by showing
normalization for Leivant's original system using hereditary substitutions and
a novel multiset ordering on types. Our development is done in the Coq proof
assistant using the Equations package, which provides an interface to define
dependently-typed programs with well-founded recursion and full dependent
pattern- matching. Equations allows us to define explicitly the hereditary
substitution function, clarifying its algorithmic behavior in presence of term
and type substitutions. From this definition, consistency can easily be
derived. The algorithmic nature of our development is crucial to reflect
languages with type quantification, enlarging the class of languages on which
reflection methods can be used in the proof assistant.Comment: In Proceedings LFMTP 2015, arXiv:1507.07597. www:
http://equations-fpred.gforge.inria.fr
Combining k-Induction with Continuously-Refined Invariants
Bounded model checking (BMC) is a well-known and successful technique for
finding bugs in software. k-induction is an approach to extend BMC-based
approaches from falsification to verification. Automatically generated
auxiliary invariants can be used to strengthen the induction hypothesis. We
improve this approach and further increase effectiveness and efficiency in the
following way: we start with light-weight invariants and refine these
invariants continuously during the analysis. We present and evaluate an
implementation of our approach in the open-source verification-framework
CPAchecker. Our experiments show that combining k-induction with
continuously-refined invariants significantly increases effectiveness and
efficiency, and outperforms all existing implementations of k-induction-based
software verification in terms of successful verification results.Comment: 12 pages, 5 figures, 2 tables, 2 algorithm
A Logic-based Approach for Recognizing Textual Entailment Supported by Ontological Background Knowledge
We present the architecture and the evaluation of a new system for
recognizing textual entailment (RTE). In RTE we want to identify automatically
the type of a logical relation between two input texts. In particular, we are
interested in proving the existence of an entailment between them. We conceive
our system as a modular environment allowing for a high-coverage syntactic and
semantic text analysis combined with logical inference. For the syntactic and
semantic analysis we combine a deep semantic analysis with a shallow one
supported by statistical models in order to increase the quality and the
accuracy of results. For RTE we use logical inference of first-order employing
model-theoretic techniques and automated reasoning tools. The inference is
supported with problem-relevant background knowledge extracted automatically
and on demand from external sources like, e.g., WordNet, YAGO, and OpenCyc, or
other, more experimental sources with, e.g., manually defined presupposition
resolutions, or with axiomatized general and common sense knowledge. The
results show that fine-grained and consistent knowledge coming from diverse
sources is a necessary condition determining the correctness and traceability
of results.Comment: 25 pages, 10 figure
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