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A Combination of Geometry Theorem Proving and Nonstandard Analysis with Application to Newton's Principia (Hardcover, 2001... A Combination of Geometry Theorem Proving and Nonstandard Analysis with Application to Newton's Principia (Hardcover, 2001 ed.)
Jacques Fleuriot
R2,732 Discovery Miles 27 320 Ships in 18 - 22 working days

Sir Isaac Newton's philosophi Naturalis Principia Mathematica'(the Principia) contains a prose-style mixture of geometric and limit reasoning that has often been viewed as logically vague.
In A Combination of Geometry Theorem Proving and Nonstandard Analysis, Jacques Fleuriot presents a formalization of Lemmas and Propositions from the Principia using a combination of methods from geometry and nonstandard analysis. The mechanization of the procedures, which respects much of Newton's original reasoning, is developed within the theorem prover Isabelle. The application of this framework to the mechanization of elementary real analysis using nonstandard techniques is also discussed.

A Combination of Geometry Theorem Proving and Nonstandard Analysis with Application to Newton's Principia (Paperback,... A Combination of Geometry Theorem Proving and Nonstandard Analysis with Application to Newton's Principia (Paperback, Softcover reprint of the original 1st ed. 2001)
Jacques Fleuriot
R2,617 Discovery Miles 26 170 Ships in 18 - 22 working days

Sir Isaac Newton's philosophi Naturalis Principia Mathematica'(the Principia) contains a prose-style mixture of geometric and limit reasoning that has often been viewed as logically vague. In A Combination of Geometry Theorem Proving and Nonstandard Analysis, Jacques Fleuriot presents a formalization of Lemmas and Propositions from the Principia using a combination of methods from geometry and nonstandard analysis. The mechanization of the procedures, which respects much of Newton's original reasoning, is developed within the theorem prover Isabelle. The application of this framework to the mechanization of elementary real analysis using nonstandard techniques is also discussed.

Automated Deduction in Geometry - 9th International Workshop, ADG 2012, Edinburgh, UK, September 17-19, 2012. Revised Selected... Automated Deduction in Geometry - 9th International Workshop, ADG 2012, Edinburgh, UK, September 17-19, 2012. Revised Selected Papers (Paperback, 2013 ed.)
Tetsuo Ida, Jacques Fleuriot
R1,294 Discovery Miles 12 940 Ships in 18 - 22 working days

This book constitutes the thoroughly refereed post-workshop proceedings of the 9th International Workshop on Automated Deduction in Geometry, ADG 2012, held in Edinburgh, UK, in September 2012. The 10 revised full papers presented together with 2 invited papers were carefully selected during two rounds of reviewing and improvement from the lectures given at the workshop. The conference represents a forum to exchange ideas and views, to present research results and progress, and to demonstrate software tools at the intersection between geometry and automated deduction; the scope of the ADG 2012 moreover has been expanded to cover topics in dynamic geometry.

Artificial Intelligence and Symbolic Computation - 13th International Conference, AISC 2018, Suzhou, China, September 16-19,... Artificial Intelligence and Symbolic Computation - 13th International Conference, AISC 2018, Suzhou, China, September 16-19, 2018, Proceedings (Paperback, 1st ed. 2018)
Jacques Fleuriot, Dongming Wang, Jacques Calmet
R1,408 Discovery Miles 14 080 Ships in 18 - 22 working days

This book constitutes the refereed proceedings of the 13th International Conference on Artificial Intelligence and Symbolic Computation, AISC 2018, held in Suzhou, China, in September 2018. The 13 full papers presented together with 5 short and 2 invited papers were carefully reviewed and selected from 31 submissions. The AISC conference is an important forum when it comes to ensuring that ideas, theoretical insights, methods and results from traditional AI can be discussed and showcased, while fostering new links with other areas of AI such as probabilistic reasoning and deep learning.

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