๐”– Scriptorium
โœฆ   LIBER   โœฆ

๐Ÿ“

Automated Model Building

โœ Scribed by Ricardo Caferra, Alexander Leitsch, Nicholas Peltier


Publisher
Springer
Year
2004
Tongue
English
Leaves
353
Series
Applied Logic Series 31
Category
Library

โฌ‡  Acquire This Volume

No coin nor oath required. For personal study only.

โœฆ Synopsis


On the history of the book: In the early 1990s several new methods and perspectives in automated deduction emerged. We just mention the superposition calculus, meta-term inference and schematization, deductive decision procedures, and automated model building. It was this last field which brought the authors of this book together. In 1994 they met at the Conference on Automated Deduction (CADE-12) in Nancy and agreed upon the general point of view, that semantics and, in particular, construction of models should play a central role in the field of automated deduction. In the following years the deduction groups of the laboratory LEIBNIZ at IMAG Grenoble and the University of Technology in Vienna organized several bilateral projects promoting this topic. This book emerged as a main result of this cooperation. The authors are aware of the fact, that the book does not cover all relevant methods of automated model building (also called model construction or model generation); instead the book focuses on deduction-based symbolic methods for the construction of Herbrand models developed in the last 12 years. Other methods of automated model building, in particular also finite model building, are mainly treated in the final chapter; this chapter is less formal and detailed but gives a broader view on the topic and a comparison of different approaches. Howtoreadthisbook: In the introduction we give an overview of automated deduction in a historical context, taking into account its relationship with the human views on formal and informal proofs.

โœฆ Table of Contents


Front Matter....Pages i-xi
Introduction....Pages 1-17
Preliminaries....Pages 19-42
Resolution-Based Methods....Pages 43-150
Constraint-Based Methods....Pages 151-232
Model Representation and Evaluation....Pages 233-272
Finite Model Building....Pages 273-318
Conclusion....Pages 319-320
Back Matter....Pages 321-344

โœฆ Subjects


Logic; Mathematical Logic and Foundations


๐Ÿ“œ SIMILAR VOLUMES


Automated Model Building
โœ Ricardo Caferra, Alexander Leitsch, Nicholas Peltier (auth.) ๐Ÿ“‚ Library ๐Ÿ“… 2004 ๐Ÿ› Springer ๐ŸŒ English

On the history of the book: In the early 1990s several new methods and perspectives in automated deduction emerged. We just mention the superposition calculus, meta-term inference and schematization, deductive decision procedures, and automated model building. It was this last field which brought th

Building information modeling : automate
โœ Nawari, Nawari O ๐Ÿ“‚ Library ๐Ÿ“… 2018 ๐Ÿ› CRC Press ๐ŸŒ English

"This book will focus on how engineers, architects, and construction practitioners can benefit from automating code checking in building design. It will focus on building regulations-checking mechanisms that are defined by the relationship among various design and engineering information management

Building information modeling : automate
โœ Nawari, Nawari O ๐Ÿ“‚ Library ๐Ÿ“… 2018 ๐Ÿ› CRC Press ๐ŸŒ English

<P>This book will focus on how engineers, architects, and construction practitioners can benefit from automating code checking in building design. It will focus on building regulations-checking mechanisms that are defined by the relationship among various design and engineering information managemen

Building Information Modeling: Automated
โœ Nawari O Nawari ๐Ÿ“‚ Library ๐Ÿ“… 2018 ๐Ÿ› CRC Press ๐ŸŒ English

This book will focus on how engineers, architects, and construction practitioners can benefit from automating code checking in building design. It will focus on building regulations-checking mechanisms that are defined by the relationship among various design and engineering information management a

Building Machine Learning Pipelines: Aut
โœ Hannes Hapke, Catherine Nelson ๐Ÿ“‚ Library ๐Ÿ“… 2020 ๐Ÿ› O'Reilly Media ๐ŸŒ English

<div><span>Companies are spending billions on machine learning projects, but it's money wasted if the models can't be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem.

Building Machine Learning Pipelines: Aut
โœ Hannes Hapke, Catherine Nelson ๐Ÿ“‚ Library ๐Ÿ“… 2020 ๐Ÿ› O'Reilly Media ๐ŸŒ English

Companies are spending billions on machine learning projects, but it's money wasted if the models can't be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You'll lear