Introduction to computer theory (revised ed.)
Introduction to computer theory (revised ed.)
Handbook of graph grammars and computing by graph transformation: volume I. foundations
Handbook of graph grammars and computing by graph transformation: volume I. foundations
Machine Learning
Introduction to the Algebraic Theory of Graph Grammars (A Survey)
Proceedings of the International Workshop on Graph-Grammars and Their Application to Computer Science and Biology
Inducing grammars from sparse data sets: a survey of algorithms and results
The Journal of Machine Learning Research
Inference of Parsable Graph Grammars for Syntactic Pattern Recognition
Fundamenta Informaticae
Grammar-based classifier system: a universal tool for grammatical inference
WSEAS Transactions on Computers
A bibliographical study of grammatical inference
Pattern Recognition
Solving coverage problems with embedded graph grammars
HSCC'07 Proceedings of the 10th international conference on Hybrid systems: computation and control
Type-2 FLCs: A New Generation of Fuzzy Controllers
IEEE Computational Intelligence Magazine
Language identification of controlled systems: modeling, control, and anomaly detection
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Knowledge base learning control system - part 1: generic architecture
AIKED'12 Proceedings of the 11th WSEAS international conference on Artificial Intelligence, Knowledge Engineering and Data Bases
Knowledge base learning control system - part 2: intelligent controller
AIKED'12 Proceedings of the 11th WSEAS international conference on Artificial Intelligence, Knowledge Engineering and Data Bases
| Hi-index | 0.00 |
Machine Learning is a computational methodology that provides automatic means of improving programmed tasks from experience. As a subfield of Machine Learning, Grammatical Inference (GI) attempts to learn structural models, such as grammars, from diverse data patterns, such as speech, artificial and natural languages, sequences provided by bioinformatics databases, amongst others. Here we are interested in identifying artificial languages from sets of positive and eventually negative samples of sentences. The present research intends to evaluate the effectiveness and usefulness of grammatical inference (GI) in control systems. The ultimate far-reaching goal addresses the issue of robots for self-assembly purposes. At least two benefits are to be rawn. First, on the epistemological level, it unifies two apparently distinct scientific communities, namely formal languages theory and robot control communities. Second, on the technological level, blending research from both fields results in the appearance of a richer community, as has been proven by the emergence of many multidisciplinary fields. Can we integrate diversified works dealing with robotic self-assembly while concentrating on grammars as an alternative control methodology? Our aim is to answer positively this central question. As far as this paper is concerned, we set out the broad methodological lines of the research while stressing the integration of these different approaches into one single unifying entity.