stl2vec: Semantic and Interpretable Vector Representation of Temporal Logic

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F24%3A00139091" target="_blank" >RIV/00216224:14330/24:00139091 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.3233/FAIA240638" target="_blank" >http://dx.doi.org/10.3233/FAIA240638</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3233/FAIA240638" target="_blank" >10.3233/FAIA240638</a>

Result language

angličtina

Original language name

stl2vec: Semantic and Interpretable Vector Representation of Temporal Logic

Original language description

Integrating symbolic knowledge and data-driven learning algorithms is a longstanding challenge in Artificial Intelligence. Despite the recognized importance of this task, a notable gap exists due to the discreteness of symbolic representations and the continuous nature of machine-learning computations. One of the desired bridges between these two worlds would be to define semantically grounded vector representation (feature embedding) of logic formulae, thus enabling to perform continuous learning and optimization in the semantic space of formulae. We tackle this goal for knowledge expressed in Signal Temporal Logic (STL) and devise a method to compute continuous embeddings of formulae with several desirable properties: the embedding (i) is finite-dimensional, (ii) faithfully reflects the semantics of the formulae, (iii) does not require any learning but instead is defined from basic principles, (iv) is interpretable. Another significant contribution lies in demonstrating the efficacy of the approach in two tasks: learning model checking, where we predict the probability of requirements being satisfied in stochastic processes; and integrating the embeddings into a neuro-symbolic framework, to constrain the output of a deep-learning generative model to comply to a given logical specification.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

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

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

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

Article name in the collection

ECAI 2024, 27th European Conference on Artificial Intelligence

ISBN

9781643685489

ISSN

0922-6389

e-ISSN

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Number of pages

8

Pages from-to

1381-1388

Publisher name

IOS Press

Place of publication

Santiago de Compostela, Spain

Event location

Santiago de Compostela, Spain

Event date

Jan 1, 2024

Type of event by nationality

CST - Celostátní akce

UT code for WoS article

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