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Artificial Intelligence and Symbolic Computation

Overview of attention for book
Cover of 'Artificial Intelligence and Symbolic Computation'

Table of Contents

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    Book Overview
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    Chapter 1 The Algorithmization of Physics: Math Between Science and Engineering
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    Chapter 2 Finite Algebras and AI: From Matrix Semantics to Stochastic Local Search
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    Chapter 3 Proof Search in Minimal Logic
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    Chapter 4 Planning and Patching Proof
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    Chapter 5 A Paraconsistent Higher Order Logic
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    Chapter 6 Abstraction Within Partial Deduction for Linear Logic
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    Chapter 7 A Decision Procedure for Equality Logic with Uninterpreted Functions
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    Chapter 8 Generic Hermitian Quantifier Elimination
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    Chapter 9 Extending Finite Model Searching with Congruence Closure Computation
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    Chapter 10 On the Combination of Congruence Closure and Completion
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    Chapter 11 Combination of Nonlinear Terms in Interval Constraint Satisfaction Techniques
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    Chapter 12 Proving and Constraint Solving in Computational Origami
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    Chapter 13 An Evolutionary Local Search Method for Incremental Satisfiability
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    Chapter 14 Solving Equations Involving Sequence Variables and Sequence Functions
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    Chapter 15 Verified Computer Algebra in Acl2
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    Chapter 16 Polynomial Interpretations with Negative Coefficients
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    Chapter 17 New Developments in Symmetry Breaking in Search Using Computational Group Theory
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    Chapter 18 Recognition of Whitehead-Minimal Elements in Free Groups of Large Ranks
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    Chapter 19 Four Approaches to Automated Reasoning with Differential Algebraic Structures
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    Chapter 20 Algorithm-Supported Mathematical Theory Exploration: A Personal View and Strategy
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    Chapter 21 An Expert System on Detection, Evaluation and Treatment of Hypertension
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    Chapter 22 Two Revision Methods Based on Constraints: Application to a Flooding Problem
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    Chapter 23 Abstraction-Driven Verification of Array Programs
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    Chapter 24 Singularities in Qualitative Reasoning
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    Chapter 25 From a Computer Algebra Library to a System with an Equational Prover
Overall attention for this book and its chapters
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About this Attention Score

  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
3 X users

Citations

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4 Dimensions

Readers on

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2 Mendeley
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Title
Artificial Intelligence and Symbolic Computation
Published by
Lecture notes in computer science, January 2004
DOI 10.1007/b100361
ISBNs
978-3-54-023212-4, 978-3-54-030210-0
Authors

Bruno Buchberger, John Campbell

Editors

Buchberger, Bruno, Campbell, John

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Professor > Associate Professor 2 100%
Readers by discipline Count As %
Computer Science 2 100%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 April 2021.
All research outputs
#8,700,096
of 25,779,988 outputs
Outputs from Lecture notes in computer science
#2,554
of 8,170 outputs
Outputs of similar age
#36,989
of 145,166 outputs
Outputs of similar age from Lecture notes in computer science
#50
of 115 outputs
Altmetric has tracked 25,779,988 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,170 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has gotten more attention than average, scoring higher than 53% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 145,166 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 115 others from the same source and published within six weeks on either side of this one. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.