Search Results for author: Dana Fisman

Found 8 papers, 0 papers with code

Learning of Structurally Unambiguous Probabilistic Grammars

no code implementations15 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.

Safety Synthesis Sans Specification

no code implementations15 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.

On the Complexity of Symbolic Finite-State Automata

no code implementations10 Nov 2020 Dana Fisman, Hadar Frenkel, Sandra Zilles

We revisit the complexity of procedures on SFAs (such as intersection, emptiness, etc.)

Learning Interpretable Models in the Property Specification Language

no code implementations10 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).

Regular omega-Languages with an Informative Right Congruence

no code implementations10 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.

SyGuS-Comp 2017: Results and Analysis

no code implementations29 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.

SyGuS-Comp 2016: Results and Analysis

no code implementations23 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.

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