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From the 1 of 5 linked papers with an AI index.

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5 papers

cs.PL2026

A Fast Quantitative Analyzer for NetKAT

Thomas Lu, Qiancheng Fu, Kevin Batz +5

The paper introduces a fast analyzer for quantitative network properties using weighted NetKAT, employing a symbolic data structure and specialized algorithms to efficiently comput…

cs.FL2026

SMT-Based Active Learning of Weighted Automata

Tiago Ferreira, Kevin Batz, Alexandra Silva

We present an SMT-based active learning algorithm for nondeterministic weighted automata (WFAs) as a practical and robust alternative to Hankel/L*-style methods. Our algorithm is p…

cs.PL2026

Weighted NetKAT: A Programming Language For Quantitative Network Verification

Emmanuel Suárez Acevedo, Tiago Ferreira, Kevin Batz +3

We introduce weighted NetKAT, a domain-specific language for modeling and verifying quantitative network properties. The language is parametric on a semiring, enabling the treatmen…

cs.NI2025

RFSeek and Ye Shall Find

Noga H. Rotman, Tiago Ferreira, Hila Peleg +2

Requests for Comments (RFCs) are extensive specification documents for network protocols, but their prose-based format and their considerable length often impede precise operationa…

cs.PL2025

Active Learning of Symbolic NetKAT Automata

Mark Moeller, Tiago Ferreira, Thomas Lu +2

NetKAT is a domain-specific programming language and logic that has been successfully used to specify and verify the behavior of packet-switched networks. This paper develops techn…