A Parametric Interpolation Framework for First-Order Theories

Kovacs, Laura; Sharygina, Natasha; Rollini, Simone Fulvio

doi:10.1007/978-3-642-45114-0_3

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Record link:

http://hdl.handle.net/20.500.12708/55046

Title:

A Parametric Interpolation Framework for First-Order Theories

Citation:

Kovacs, L., Sharygina, N., & Rollini, S. F. (2013). A Parametric Interpolation Framework for First-Order Theories. In F. Castro, A. Gelbukh, & M. Gonzalez (Eds.), Advances in Artificial Intelligence and Its Applications (pp. 24–40). Springer LNCS 8265. https://doi.org/10.1007/978-3-642-45114-0_3

Publisher DOI:

10.1007/978-3-642-45114-0_3

Publication Type:

Inproceedings - Full-Paper Contribution

Authors:

Kovacs, Laura
Sharygina, Natasha
Rollini, Simone Fulvio

Organisational Unit:

E194-05 - Forschungsbereich Compilers and Languages

Published in:

Advances in Artificial Intelligence and Its Applications

Date (published):

2013