Artificial Intelligence Applications and Innovations: 3rd by Sotiris B. Kotsiantis (auth.), Ilias Maglogiannis, Kostas

By Sotiris B. Kotsiantis (auth.), Ilias Maglogiannis, Kostas Karpouzis, Max Bramer (eds.)

International Federation for info Processing

The IFIP sequence publishes state of the art ends up in the sciences and applied sciences of knowledge and verbal exchange. The scope of the sequence comprises: foundations of desktop technological know-how; software program thought and perform; schooling; machine functions in know-how; conversation platforms; platforms modeling and optimization; info structures; desktops and society; desktops expertise; safeguard and safeguard in details processing platforms; man made intelligence; and human-computer interplay. complaints and post-proceedings of referred overseas meetings in machine technological know-how and interdisciplinary fields are featured. those effects frequently precede magazine book and characterize the most up-tp-date examine. The relevant target of the IFIP sequence is to inspire schooling and the dissemination and alternate of data approximately all points of computing.

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Extra info for Artificial Intelligence Applications and Innovations: 3rd IFIP Conference on Artificial Intelligence Applications and Innovations (AIAI) 2006, June 7–9, 2006, Athens, Greece

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Theoretical Principles of Distance Education. Routledge, London. 4. Open Leaning (2004). Special issues on "Student retention in open and distance learning". asp. 5. , Pierrakeas, Ch. & Pintelas, P. (2002). A survey on student dropout rates and dropout causes concerning the students in the Course of Informatics of the Hellenic Open University. Computers & Education, 39, 361-377. 6. Mitchell, T. (1997). Machine Learning. McGraw Hill. 7. R. (1991). Concept formation and decision tree induction using the genetic programming paradigm.

After the emergence of A^2 new vectors, the neural network is retrained with all the N1+N2 vectors, and after each iteration the visualization error E(Nj-^N2) is calculated and the computing time is measured. The new set of SAMANN network weights CD2 is found. 2. The SAMANN network is trained by Nj initial vectors, a set of weights co^ is obtained, and the visualization error E(Nj) is calculated. Since in order to renew the weights co, a pair of vectors // and v is simultaneously provided for the neural network, the neural network is retrained with 2*N2 vectors at each iteration: at each step of training one vector is taken from the primary dataset and the other from the new one.

Our research work involves the application of such a hybrid method in educational mobile robotics for two main types of navigation experiments: simulations and real-world experiments that use a toy mobile robot, ROBO, developed at University of Ploiesti. In our experiments we have considered indoor environments such as offices and homes. The paper is organized as follows. Section 2 describes the robot navigation task. The intelligent educational mobile robot system is presented in section 3. The architecture of the system as well as the ontology with its associated knowlede base, and adaptation method are detailed.

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