Property Invariant Embedding for Automated Reasoning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F20%3A00346155" target="_blank" >RIV/68407700:21730/20:00346155 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.3233/FAIA200244" target="_blank" >https://doi.org/10.3233/FAIA200244</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3233/FAIA200244" target="_blank" >10.3233/FAIA200244</a>

Result language

angličtina

Original language name

Property Invariant Embedding for Automated Reasoning

Original language description

Automated reasoning and theorem proving have recently become major challenges for machine learning. In other domains, representations that are able to abstract over unimportant transformations, such as abstraction over translations and rotations in vision, are becoming more common. Standard methods of embedding mathematical formulas for learning theorem proving are however yet unable to handle many important transformations. In particular, embedding previously unseen labels, that often arise in definitional encodings and in Skolemization, has been very weak so far. Similar problems appear when transferring knowledge between known symbols. We propose a novel encoding of formulas that extends existing graph neural network models. This encoding represents symbols only by nodes in the graph, without giving the network any knowledge of the original labels. We provide additional links between such nodes that allow the network to recover the meaning and therefore correctly embed such nodes irrespective of the given labels. We test the proposed encoding in an automated theorem prover based on the tableaux connection calculus, and show that it improves on the best characterizations used so far. The encoding is further evaluated on the premise selection task and a newly introduced symbol guessing task, and shown to correctly predict 65% of the symbol names.

Czech name

—

Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/EF15_003%2F0000466" target="_blank" >EF15_003/0000466: Artificial Intelligence and Reasoning</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

The proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020)

ISBN

978-1-64368-100-9

ISSN

0922-6389

e-ISSN

1879-8314

Number of pages

8

Pages from-to

1395-1402

Publisher name

IOS Press

Place of publication

Oxford

Event location

Virtual online

Event date

Aug 29, 2020

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000650971301082