Download e-book for iPad: Agents and Artificial Intelligence: Second International by Zareen Syed, Tim Finin (auth.), Joaquim Filipe, Ana Fred,

By Zareen Syed, Tim Finin (auth.), Joaquim Filipe, Ana Fred, Bernadette Sharp (eds.)

ISBN-10: 3642198899

ISBN-13: 9783642198892

This ebook constitutes the completely refereed post-conference complaints of the second one overseas convention on brokers and synthetic Intelligence, ICAART 2010, held in Valencia, Spain, in January 2010. The 17 revised complete papers provided including an invited paper have been rigorously reviewed and chosen from 364 submissions. comparable because the convention the papers are geared up in simultaneous tracks: synthetic Intelligence and brokers. the chosen papers mirror the interdisciplinary nature of the convention. the range of themes is a crucial function of this convention, permitting an total notion of numerous vital medical and technological trends.

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Extra info for Agents and Artificial Intelligence: Second International Conference, ICAART 2010, Valencia, Spain, January 22-24, 2010. Revised Selected Papers

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38(11), 33–38 (1995) Part I Artificial Intelligence Language Support to XML Data Mining: A Case Study Andrea Romei and Franco Turini Department of Computer Science, University of Pisa Largo B. it Abstract. There are several reasons that justify the study of a powerful, expressive and efficient XML-based framework for intelligent data analysis. First of all, the proliferation of XML sources offer good opportunities to mine new data. Second, native XML databases appear to be a natural alternative to relational databases when the purpose is querying both data and the extracted models in an uniform manner.

1 Preselection During the preselection vertical line pairs generated by the door frame represent door candidates for the AdaBoost algorithm. To receive vertical lines we apply the Contour Following Algorithm [11]. Compared to other transformations, this method has the advantage that we obtain the starting and end points of these lines. 42 J. Hensler, M. Blaich, and O. Bittel Fig. 3. This Image illustrates our approach to find doors. Several weak classifiers are combined using the AdaBoost algorithm in the training process.

However, by combining them with the aid of AdaBoost Algorithm more than 82% of all doors with a false positive rate less than 3% are detected in static test data. Further improvement can easily be achieved by using different door perspectives from a moving robot. In a realtime mobile robot application we detect more than 90% of all doors with a very low false detection rate. Keywords: Door detection, AdaBoost, Learning algorithm, Mobile robot. 1 Introduction In an indoor environment doors constitute significant landmarks.

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Agents and Artificial Intelligence: Second International Conference, ICAART 2010, Valencia, Spain, January 22-24, 2010. Revised Selected Papers by Zareen Syed, Tim Finin (auth.), Joaquim Filipe, Ana Fred, Bernadette Sharp (eds.)


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