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By Ning Zhong, Jiming Liu, Yiyu Yao

Web Intelligence is a brand new course for medical learn and improvement that explores the elemental roles in addition to sensible affects of synthetic intelligence and complex details know-how for the subsequent iteration of Web-empowered structures, prone, and environments. internet Intelligence is considered the major examine box for the improvement of the knowledge internet (including the Semantic Web).

As the 1st publication dedicated to net Intelligence, this coherently written multi-author monograph offers an intensive advent and a scientific evaluate of this new box. It provides either the present country of analysis and improvement in addition to program facets. The booklet should be a precious and lasting resource of reference for researchers and builders attracted to internet Intelligence. scholars and builders will also relish the varied illustrations and examples.

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Huberman: Technical comment to 'Emergence of scaling in random networks'. 3 E. Adar, B. A. Huberman: The economics of surfing. 4 R. Albert, H. Jeong, A. L. Barabasi: Diameter of World-Wide Web. 5 M. F. Arlitt, C. L. Williamson: Web server workload characterization: The search for invariants. Proc. the ACM SIGMETRICS'96 Conference, Philadelphia, PA (ACM Press, New York, 1996) pp. 6 A. L. Barabasi, R. Albert: Emergence of scaling in random networks. 7 A. L. Barabasi, R. Albert, H. Jeong: Scale-free characteristics of random networks: The topology of the World-Wide Web.

Zhang, Y. Ye 1. Agent persistence in foraging will normally decay along with the time or steps spent on foraging. 16) where a and 'Y are positive constant factors. 2. 16]. L,at). 17) The reward for finding some desirable information that an agent will receive at each step is considered as being proportional to how much information that the agent accepts. In our foraging agent model, since the changing of the agent interest vector corresponds to the amount of information that the agent accepts, the reward function can be defined as follows: M LlR =L (vui(t- 1)- vui(t)).

Or, if it finds sufficient information resource, it will stop exploring further. All autonomous agents start to forage from a certain page, which is connected to other pages on various topics. The behavior of agent foraging can be described with the following algorithm: Initialize network Initialize agent profile For each agent i While agent support S < Smu: and S > Smin Find the links within node k that agent locates Select direction (goal node) of next step Surf goal node Update agent motivational support based on Eq.

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