Learning fallible Deterministic Finite Automata

Adaptive E-learning using Deterministic Finite Automata

Adaptive E-learning refers to adapt the way of presentation of educational material according to the student's needs. Understanding ability differs by student to student so the learning path should vary according to their understanding ability. Some students may understand by once some may needs more with different way. This paper represents a same topic with various approaches to the diff...

Active Coevolutionary Learning of Deterministic Finite Automata

This paper describes an active learning approach to the problem of grammatical inference, specifically the inference of deterministic finite automata (DFAs). We refer to the algorithm as the estimation-exploration algorithm (EEA). This approach differs from previous passive and active learning approaches to grammatical inference in that training data is actively proposed by the algorithm, rathe...

Learning Deterministic Finite Automata from Infinite Alphabets

We proposes an algorithm to learn automata infinite alphabets, or at least too large to enumerate. We apply it to define a generic model intended for regression, with transitions constrained by intervals over the alphabet. The algorithm is based on the Red & Blue framework for learning from an input sample. We show two small case studies where the alphabets are respectively the natural and real...

Failure Deterministic Finite Automata

Inspired by failure functions found in classical pattern matching algorithms, a failure deterministic finite automaton (FDFA) is defined as a formalism to recognise a regular language. An algorithm, based on formal concept analysis, is proposed for deriving from a given deterministic finite automaton (DFA) a language-equivalent FDFA. The FDFA’s transition diagram has fewer arcs than that of the...

Learning Regular Languages Using Non Deterministic Finite Automata