Mining State-Based Models from Proof Corpora

نویسندگان

  • Thomas Gransden
  • Neil Walkinshaw
  • Rajeev Raman
چکیده

Interactive theorem provers have been used extensively to reason about various software/hardware systems and mathematical theorems. The key challenge when using an interactive prover is finding a suitable sequence of proof steps that will lead to a successful proof requires a significant amount of human intervention. This paper presents an automated technique that takes as input examples of successful proofs and infers an Extended Finite State Machine as output. This can in turn be used to generate proofs of new conjectures. Our preliminary experiments show that the inferred models are generally accurate (contain few false-positive sequences) and that representing existing proofs in such a way can be very useful when guiding new ones.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

SEPIA: Search for Proofs Using Inferred Automata

This paper describes SEPIA, a tool for automated proof generation in Coq. SEPIA combines model inference with interactive theorem proving. Existing proof corpora are modelled using state-based models inferred from tactic sequences. These can then be traversed automatically to identify proofs. The SEPIA system is described and its performance evaluated on three Coq datasets. Our results show tha...

متن کامل

Bluima: a UIMA-based NLP Toolkit for Neuroscience

This paper describes Bluima, a natural language processing (NLP) pipeline focusing on the extraction of neuroscientific content and based on the UIMA framework. Bluima builds upon models from biomedical NLP (BioNLP) like specialized tokenizers and lemmatizers. It adds further models and tools specific to neuroscience (e.g. named entity recognizer for neuron or brain region mentions) and provide...

متن کامل

Formal proof mining, a structure-oriented approach

Large corpora of formal proofs have been developed over the years, that sit in the repositories of the various existing proof assistants. We advocate that these databases could be analysed by means of data-mining techniques, to help both the improvement of these tools on their usage, and the proof automation they provide. We suggest a technique for the analysis, frequent subtree mining, review ...

متن کامل

Making adjustments to event annotations for improved biological event extraction

BACKGROUND Current state-of-the-art approaches to biological event extraction train statistical models in a supervised manner on corpora annotated with event triggers and event-argument relations. Inspecting such corpora, we observe that there is ambiguity in the span of event triggers (e.g., "transcriptional activity" vs. 'transcriptional'), leading to inconsistencies across event trigger anno...

متن کامل

Proof Mining with Dependent Types

Several approaches exist to data-mining big corpora of formal proofs. Some of these approaches are based on statistical machine learning, and some – on theory exploration. However, most are developed for either untyped or simply-typed theorem provers. In this paper, we present a method that combines statistical data mining and theory exploration in order to analyse and automate proofs in depend...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2014