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Intelligent Computer Mathematics : 13th International Conference, CICM 2020, Bertinoro, Italy, July 26–31, 2020, Proceedings
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Editor(s):
Christoph Benzmüller
,
Bruce Miller
Publication date
(Print):
2020
Publisher:
Springer International Publishing
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Contributors
Christoph Benzmüller:
(View ORCID Profile)
Bruce Miller:
(View ORCID Profile)
Book
ISBN (Print):
978-3-030-53517-9
ISBN (Electronic):
978-3-030-53518-6
Publication date (Print):
2020
DOI:
10.1007/978-3-030-53518-6
SO-VID:
54489a94-2185-4bc0-82b3-9caf504d607a
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Book chapters
pp. 3
A Promising Path Towards Autoformalization and General Artificial Intelligence
pp. 23
Formal Adventures in Convex and Conical Spaces
pp. 39
Towards a Heterogeneous Query Language for Mathematical Knowledge
pp. 55
Leveraging the Information Contained in Theory Presentations
pp. 71
Metamath Zero: Designing a Theorem Prover Prover
pp. 89
Adding an Abstraction Barrier to ZF Set Theory
pp. 105
A Framework for Formal Dynamic Dependability Analysis Using HOL Theorem Proving
pp. 123
Induction with Generalization in Superposition Reasoning
pp. 138
A Survey of Languages for Formalizing Mathematics
pp. 157
OntoMath\({}^{Edu}\): A Linguistically Grounded Educational Mathematical Ontology
pp. 173
FrameIT: Detangling Knowledge Management from Game Design in Serious Games
pp. 190
Formalizing Graph Trail Properties in Isabelle/HOL
pp. 206
Representing Structural Language Features in Formal Meta-languages
pp. 222
Formally Verifying Proofs for Algebraic Identities of Matrices
pp. 237
AutoMSC: Automatic Assignment of Mathematics Subject Classification Labels
pp. 251
Maintaining a Library of Formal Mathematics
pp. 271
The Tactician
pp. 278
Tree Neural Networks in HOL4
pp. 284
Interpreting Mathematical Texts in Naproche-SAD
pp. 290
TGView3D: A System for 3-Dimensional Visualization of Theory Graphs
pp. 297
Simple Dataset for Proof Method Recommendation in Isabelle/HOL
pp. 303
Dataset Description: Formalization of Elementary Number Theory in Mizar
pp. 309
Guiding Inferences in Connection Tableau by Recurrent Neural Networks
pp. 315
First Neural Conjecturing Datasets and Experiments
pp. 324
A Contextual and Labeled Math-Dataset Derived from NIST’s DLMF
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