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A Multi-agent System Based on Evolutionary Learning for the Usability Analysis of Websites

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Intelligent Agents in the Evolution of Web and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 167))

Abstract

In this chapter we propose a novel multi-agent system for the semi-automated study of the usability of websites, an increasingly critical issue given the ubiquity of the Web and its technological evolution. The proposed system constructs a key phrase-based model of the users trying to reach one URL from another, simulates the browsing process, and analyses the web pages in the path. The resulting usability analysis is focused on issues such as navigation paths, links, page content, HTML coding, and accessibility. Our system automatically draws usability conclusions and suggestions and also presents significant data in support of the human usability expert. The architecture of the system consists of rule-based reactive agents subject to evolutionary processes. The application of evolution allows the agents to explore possible solutions in a more realistic way than either exhaustive or arbitrary examinations.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of A Coruña, Campus de Elviña, 15071, A Coruña, Spain

    Eduardo Mosqueira-Rey, David Alonso-Ríos, Ana Vázquez-García, Belén Baldonedo del Río & Vicente Moret-Bonillo

Authors
  1. Eduardo Mosqueira-Rey
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  2. David Alonso-Ríos
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  3. Ana Vázquez-García
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  4. Belén Baldonedo del Río
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  5. Vicente Moret-Bonillo
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Editor information

Editors and Affiliations

  1. Wroclaw University of Technolgy, Poland

    Ngoc Thanh Nguyen

  2. University of South Australia, Australia

    Lakhmi C. Jain

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Mosqueira-Rey, E., Alonso-Ríos, D., Vázquez-García, A., del Río, B.B., Moret-Bonillo, V. (2009). A Multi-agent System Based on Evolutionary Learning for the Usability Analysis of Websites. In: Nguyen, N.T., Jain, L.C. (eds) Intelligent Agents in the Evolution of Web and Applications. Studies in Computational Intelligence, vol 167. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88071-4_2

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  • DOI: https://doi.org/10.1007/978-3-540-88071-4_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88070-7

  • Online ISBN: 978-3-540-88071-4

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