The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON COMPUTERS
Transactions ID Number: 29-113
Full Name: Aboubekeur Hamdi-Cherif
Position: Associate Professor
Age: ON
Sex: Male
Address: Qassim University, Computer College, PO Box 6688 - Buraydah 51452
Country: SAUDI ARABIA
Tel: 0096663800050
Tel prefix: 4033
Fax:
E-mail address: elhamdi62@gmail.com
Other E-mails: shrief@qu.edu.sa,elhamdi62@hotmail.com,chafikmo@yahoo.com
Title of the Paper: Grammatical inference methodology for control systems
Authors as they appear in the Paper: Chafia Kara-Mohamed (alias Hamdi-Cherif)
Email addresses of all the authors: elhamdi62@gmail.com,elhamdi62@hotmail.com,chafikmo@yahoo.com
Number of paper pages: 10
Abstract: 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 drawn. 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.
Keywords: Machine learning, robot control, grammatical inference, graph grammar, formal languages for control, self-assembly, intelligent control, emergent control technologies
EXTENSION of the file: .doc
Special (Invited) Session: Grammatical inference for robotic self-assembly - basic methodology
Organizer of the Session: 609-734
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