Chapter

Multi-Agent Systems and Applications IV

Volume 3690 of the series Lecture Notes in Computer Science pp 579-582

Selection in Scale-Free Small World

  • Zsolt PalotaiAffiliated withDepartment of Information Systems, Eötvös Loránd University
  • , Csilla FarkasAffiliated withDepartment of Computer Sciences and Engineering, University of South Carolina
  • , András LőrinczAffiliated withDepartment of Information Systems, Eötvös Loránd University

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Abstract

In this paper we compare our selection based learning algorithm with the reinforcement learning algorithm in Web crawlers. The task of the crawlers is to find new information on the Web. We performed simulations based on data collected from the Web. The collected portion of the Web is typical and exhibits scale-free small world (SFSW) structure. We have found that on this SFSW, the weblog update algorithm performs better than the reinforcement learning algorithm. It finds the new information faster than the reinforcement learning algorithm and has better new information/all submitted documents ratio.