Adaptive E-learning using 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...

Learning Regular Languages Using Non Deterministic Finite Automata

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...

Evolving Deterministic Finite Automata Using Cellular Encoding

This paper presents a method for the evolution of deterministic finite automata that combines genetic programming and cellular encoding. Programs are evolved that specify actions for the incremental growth of a deterministic finite automata from an initial single-state zygote. The results show that, given a test bed of positive and negative samples, the proposed method is successful at inducing...