no code implementations • 25 Jun 2023 • Dana Fisman, Noa Izsak, Swen Jacobs
The problem of learning a computational model from examples has been receiving growing attention.
no code implementations • 17 Mar 2022 • Dana Fisman, Dolav Nitay, Michal Ziv-Ukelson
In this work we address the first problem.
no code implementations • 15 Nov 2020 • Dolav Nitay, Dana Fisman, Michal Ziv-Ukelson
We show that the learned CMTA can be converted into a probabilistic grammar, thus providing a complete algorithm for learning a structurally unambiguous probabilistic context free grammar (both the grammar topology and the probabilistic weights) using structured membership queries and structured equivalence queries.
no code implementations • 15 Nov 2020 • Roderick Bloem, Hana Chockler, Masoud Ebrahimi, Dana Fisman, Heinz Riener
We define the problem of learning a transducer ${S}$ from a target language $U$ containing possibly conflicting transducers, using membership queries and conjecture queries.
no code implementations • 10 Nov 2020 • Dana Fisman, Hadar Frenkel, Sandra Zilles
We revisit the complexity of procedures on SFAs (such as intersection, emptiness, etc.)
no code implementations • 10 Feb 2020 • Rajarshi Roy, Dana Fisman, Daniel Neider
In contrast to most of the recent work in this area, which focuses on descriptions expressed in Linear Temporal Logic (LTL), we develop a learning algorithm for formulas in the IEEE standard temporal logic PSL (Property Specification Language).
no code implementations • 10 Sep 2018 • Dana Angluin, Dana Fisman
The right congruence of a regular omega-language is not informative enough; many regular omega-languages have a trivial right congruence, and in general it is not always possible to define an omega-automaton recognizing a given language that is isomorphic to the rightcon automaton.
no code implementations • 29 Nov 2017 • Rajeev Alur, Dana Fisman, Rishabh Singh, Armando Solar-Lezama
Syntax-Guided Synthesis (SyGuS) is the computational problem of finding an implementation f that meets both a semantic constraint given by a logical formula phi in a background theory T, and a syntactic constraint given by a grammar G, which specifies the allowed set of candidate implementations.
no code implementations • 23 Nov 2016 • Rajeev Alur, Dana Fisman, Rishabh Singh, Armando Solar-Lezama
Syntax-Guided Synthesis (SyGuS) is the computational problem of finding an implementation f that meets both a semantic constraint given by a logical formula $\varphi$ in a background theory T, and a syntactic constraint given by a grammar G, which specifies the allowed set of candidate implementations.
