Machine Learning for Automated Theorem Proving

Machine Learning for Automated Theorem Proving
Author: Sean B. Holden
Publisher:
Total Pages: 202
Release: 2021-11-22
Genre:
ISBN: 9781680838985

In this book, the author presents the results of his thorough and systematic review of the research at the intersection of two apparently rather unrelated fields: Automated Theorem Proving (ATP) and Machine Learning (ML).

Automated Reasoning

Automated Reasoning
Author: Alessandro Armando
Publisher: Springer Science & Business Media
Total Pages: 568
Release: 2008-07-25
Genre: Computers
ISBN: 3540710698

methods, description logics and related logics, sati?ability modulo theory, decidable logics, reasoning about programs, and higher-order logics.

Understanding Machine Learning

Understanding Machine Learning
Author: Shai Shalev-Shwartz
Publisher: Cambridge University Press
Total Pages: 415
Release: 2014-05-19
Genre: Computers
ISBN: 1107057132

Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

A Machine Program for Theorem-proving

A Machine Program for Theorem-proving
Author: Martin Davis
Publisher:
Total Pages: 40
Release: 1961
Genre: Calculus of variations
ISBN:

The programming of a proof procedure is discussed in connection with trial runs and possible improvements. (Author).

Concrete Semantics

Concrete Semantics
Author: Tobias Nipkow
Publisher: Springer
Total Pages: 304
Release: 2014-12-03
Genre: Computers
ISBN: 3319105426

Part I of this book is a practical introduction to working with the Isabelle proof assistant. It teaches you how to write functional programs and inductive definitions and how to prove properties about them in Isabelle’s structured proof language. Part II is an introduction to the semantics of imperative languages with an emphasis on applications like compilers and program analysers. The distinguishing feature is that all the mathematics has been formalised in Isabelle and much of it is executable. Part I focusses on the details of proofs in Isabelle; Part II can be read even without familiarity with Isabelle’s proof language, all proofs are described in detail but informally. The book teaches the reader the art of precise logical reasoning and the practical use of a proof assistant as a surgical tool for formal proofs about computer science artefacts. In this sense it represents a formal approach to computer science, not just semantics. The Isabelle formalisation, including the proofs and accompanying slides, are freely available online, and the book is suitable for graduate students, advanced undergraduate students, and researchers in theoretical computer science and logic.

Interactive Theorem Proving and Program Development

Interactive Theorem Proving and Program Development
Author: Yves Bertot
Publisher: Springer Science & Business Media
Total Pages: 492
Release: 2013-03-14
Genre: Mathematics
ISBN: 366207964X

A practical introduction to the development of proofs and certified programs using Coq. An invaluable tool for researchers, students, and engineers interested in formal methods and the development of zero-fault software.

Mathematics for Machine Learning

Mathematics for Machine Learning
Author: Marc Peter Deisenroth
Publisher: Cambridge University Press
Total Pages: 392
Release: 2020-04-23
Genre: Computers
ISBN: 1108569323

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

Bayesian Reasoning and Machine Learning

Bayesian Reasoning and Machine Learning
Author: David Barber
Publisher: Cambridge University Press
Total Pages: 739
Release: 2012-02-02
Genre: Computers
ISBN: 0521518148

A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.

Automated Theory Formation in Pure Mathematics

Automated Theory Formation in Pure Mathematics
Author: Simon Colton
Publisher: Springer Science & Business Media
Total Pages: 384
Release: 2012-12-06
Genre: Mathematics
ISBN: 1447101472

In recent years, Artificial Intelligence researchers have largely focused their efforts on solving specific problems, with less emphasis on 'the big picture' - automating large scale tasks which require human-level intelligence to undertake. The subject of this book, automated theory formation in mathematics, is such a large scale task. Automated theory formation requires the invention of new concepts, the calculating of examples, the making of conjectures and the proving of theorems. This book, representing four years of PhD work by Dr. Simon Colton demonstrates how theory formation can be automated. Building on over 20 years of research into constructing an automated mathematician carried out in Professor Alan Bundy's mathematical reasoning group in Edinburgh, Dr. Colton has implemented the HR system as a solution to the problem of forming theories by computer. HR uses various pieces of mathematical software, including automated theorem provers, model generators and databases, to build a theory from the bare minimum of information - the axioms of a domain. The main application of this work has been mathematical discovery, and HR has had many successes. In particular, it has invented 20 new types of number of sufficient interest to be accepted into the Encyclopaedia of Integer Sequences, a repository of over 60,000 sequences contributed by many (human) mathematicians.

Handbook of Practical Logic and Automated Reasoning

Handbook of Practical Logic and Automated Reasoning
Author: John Harrison
Publisher: Cambridge University Press
Total Pages: 703
Release: 2009-03-12
Genre: Computers
ISBN: 0521899575

A one-stop reference, self-contained, with theoretical topics presented in conjunction with implementations for which code is supplied.