E Equational Theorem Prover (Paperback)


High Quality Content by WIKIPEDIA articles E is a modern, high performance theorem prover for full first-order logic with equality. It is based on the equational superposition calculus and uses a purely equational paradigm. It has been integrated into other theorem provers and it has been among the best-placed systems in several theorem proving competitions. E was developed by Stephan Schulz, originally in the Automated Reasoning Group at TU Munich. The system is based on the equational superposition calculus. In contrast to most other current provers, the implementation actually uses a purely equational paradigm, and simulates non-equational inferences via appropriate equality inferences. Significant innovations include shared term rewriting (where many possible equational simplifications are carried out in a single operation), several efficient term indexing data structures for speeding up inferences, advanced inference literal selection strategies, and various uses of machine learning techniques to improve the search behaviour.

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Product Description

High Quality Content by WIKIPEDIA articles E is a modern, high performance theorem prover for full first-order logic with equality. It is based on the equational superposition calculus and uses a purely equational paradigm. It has been integrated into other theorem provers and it has been among the best-placed systems in several theorem proving competitions. E was developed by Stephan Schulz, originally in the Automated Reasoning Group at TU Munich. The system is based on the equational superposition calculus. In contrast to most other current provers, the implementation actually uses a purely equational paradigm, and simulates non-equational inferences via appropriate equality inferences. Significant innovations include shared term rewriting (where many possible equational simplifications are carried out in a single operation), several efficient term indexing data structures for speeding up inferences, advanced inference literal selection strategies, and various uses of machine learning techniques to improve the search behaviour.

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Product Details

General

Imprint

Betascript Publishing

Country of origin

United States

Release date

August 2010

Availability

Supplier out of stock. If you add this item to your wish list we will let you know when it becomes available.

First published

August 2010

Editors

, ,

Dimensions

152 x 229 x 5mm (L x W x T)

Format

Paperback - Trade

Pages

84

ISBN-13

978-6131962011

Barcode

9786131962011

Categories

LSN

6131962014



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