A Family of Algorithms for Non Deterministic Regular Languages Inference

Parga, Manuel Vázquez de; Garcia, Pedro Castillo; Ruiz, José

doi:10.1007/11812128_25

Cited by 12 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…fully nondeterministic) finite automata is treated by de Parga, García and Ruiz (2006). fully nondeterministic) finite automata is treated by de Parga, García and Ruiz (2006).…”

Section: The Consistency Problemmentioning

confidence: 99%

See 1 more Smart Citation

A randomised inference algorithm for regular tree languages

gberg

2011

Nat. Lang. Eng.

View full text Add to dashboard Cite

Link to this article: http://journals.cambridge.org/abstract_S1351324911000064 How to cite this article: JOHANNA HÖGBERG (2011). A randomised inference algorithm for regular tree languages. AbstractWe present a randomised inference algorithm for regular tree languages. The algorithm takes as input two disjoint finite nonempty sets of trees P and N and outputs a nondeterministic finite tree automaton that accepts every tree in P and rejects every tree in N. The output automaton typically represents a nontrivial generalisation of the examples given in P and N. To obtain compact output automata, we use a heuristics similar to bisimulation minimisation. The algorithm has time complexity of O n N · n 2 P , where n N and n P are the size of N and P, respectively. Experiments are conducted on a prototype implementation, and the empirical results appear to second the theoretical results.

show abstract

“…fully nondeterministic) finite automata is treated by de Parga, García and Ruiz (2006). fully nondeterministic) finite automata is treated by de Parga, García and Ruiz (2006).…”

Section: The Consistency Problemmentioning

confidence: 99%

“…One direction for future work is to lift the family of algorithms described by de Parga et al (2006) to the domain of trees and to compare their performance with that of Algorithm 1. From a theoretical point of view, it would also be nice if Algorithm 1 could be modified to allow identification in the limit.…”

Section: Future Workmentioning

confidence: 99%

A randomised inference algorithm for regular tree languages

gberg

2011

Nat. Lang. Eng.

View full text Add to dashboard Cite

show abstract

“…Ad-hoc algorithms, such as DeLeTe2 [4], are based on merging states from the PTA. More recently, a new family of algorithms for regular languages inference was given in [5]. Some approaches are based on metaheuristics, such as in [6] where hill-climbing is applied in the context of regular languages, or [7] which is based on a genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Taking Advantage of a Very Simple Property to Efficiently Infer NFAs

Jastrząb

Lardeux

Monfroy

2022

2022 IEEE 34th International Conference on Tools With Artificial Intelligence (ICTAI)

View full text Add to dashboard Cite

Grammatical inference consists in learning a formal grammar as a finite state machine or as a set of rewrite rules. In this paper, we are concerned with inferring Nondeterministic Finite Automata (NFA) that must accept some words, and reject some other words from a given sample. This problem can naturally be modeled in SAT. The standard model being enormous, some models based on prefixes, suffixes, and hybrids were designed to generate smaller SAT instances.There is a very simple and obvious property that says: if there is an NFA of size k for a given sample, there is also an NFA of size k+1. We first strengthen this property by adding some characteristics to the NFA of size k+1. Hence, we can use this property to tighten the bounds of the size of the minimal NFA for a given sample. We then propose simplified and refined models for NFA of size k+1 that are smaller than the initial models for NFA of size k. We also propose a reduction algorithm to build an NFA of size k from a specific NFA of size k+1. Finally, we validate our proposition with some experimentation that shows the efficiency of our approach.

show abstract

“…The problem of learning formal grammars from a given sample of words has been explored from multiple angles. Several algorithms have been proposed, including ad-hoc methods such as DeLeTe2 [2] that focuses on merging states from the prefix tree acceptor (PTA), and newer approaches like the family of algorithms for regular languages inference presented in [18]. Some studies have employed metaheuristics, such as hill-climbing in [16], while others have used complete solvers that can always find a solution if one exists, prove unsatisfiability, and find the global optimum in optimization problems.…”

mentioning

confidence: 99%

Inference of Over-Constrained NFA of Size $$k+1$$ to Efficiently and Systematically Derive NFA of Size k for Grammar Learning

Jastrząb

Lardeux

Monfroy

2023

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Grammatical inference involves learning a formal grammar as a finite state machine or set of rewrite rules. This paper focuses on inferring Nondeterministic Finite Automata (NFA) from a given sample of words: the NFA must accept some words, and reject others. Our approach is unique in that it addresses the question of whether or not a finite automaton of size k exists for a given sample by using an overconstrained model of size k + 1. Additionally, our method allows for the identification of the automaton of size k when it exists. While the concept may seem straightforward, the effectiveness of this approach is demonstrated through the results of our experiments.

show abstract

A Family of Algorithms for Non Deterministic Regular Languages Inference

Cited by 12 publications

References 4 publications

A randomised inference algorithm for regular tree languages

A randomised inference algorithm for regular tree languages

Taking Advantage of a Very Simple Property to Efficiently Infer NFAs

Inference of Over-Constrained NFA of Size $$k+1$$ to Efficiently and Systematically Derive NFA of Size k for Grammar Learning

Contact Info

Product

Resources

About