Combining Information from Multiple Internet Sources

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It may happen that the algorithm from listing 3.1.4 will not be started at all; in case of disjoint result sets. If it is so, Manager Agent creates the combined result set from all result sets (without repetitions) and such is returned to the web application. Manager Agent iterates through each of the initial result sets, takes every URL which is not already in the “big result set” and populates the set with this URL. Such situation occurs very rarely, but if one takes only 2 engines – one of Polish origin and one of English and issue a specific query such situation may happen – the result sets will be totally disjoint. If such situation happens, feedback should be provided so that the application can calculate the weights (listing 3.4.2) according to the URL provided as feedback. Weights are then used to rank the search engines. Each of the URL presented can be opened, look through its content and then finally the URL can be marked as the best one. Afterwards the weights are calculated using the URL which was marked as the feedback and which is an anchor to the weights calculation algorithm. It may also happen that algorithm from 3.4.1 will remove some result sets from the evaluation. The result sets which were completely disjoint with other result sets are removed as being unsuitable for negotiation. This process needs an opinion of every search engine on each URL that is a part of the competition. If, however, the sets are joint in even one URL they are considered suitable for negotiation and algorithm 3.4.1 will update those with the URLs those are missing. The missing URLs are taken from the result sets of other search engines. Then the main part deals only with the result sets that were left. After the main part is completed, the search engine ranking can be performed by invoking the algorithm from listing 3.4.2 for weights calculation, taking the first yielded answer URL as an anchor point for the calculations. Then the weights are sent to the corresponding Search Agents which in turn update the knowledge base with weight of the his search engine selected at the beginning and for this particular query. Simultaneously, the final result set containing 10 selected URLs is returned. Below is an activity diagram that represents the algorithm processing flow. This diagram presents processing flow as a whole. The diagram is divided into three parts in order to expose application responsibility during the process. 26
Fig 3.1.1 Game theory Method Activity Diagram 27
Page 1 and 2: WARSAW UNIVERSITY OF TECHNOLOGY FAC
Page 3 and 4: POLITECHNIKA WARSZAWSKA WYDZIAŁ MA
Page 5 and 6: Combining Information from Multiple
Page 7 and 8: Table of contents 1. Introduction..
Page 9 and 10: application presented in this paper
Page 11 and 12: 2. Design of the tool used for test
Page 13 and 14: 2.2.1 Design of the Client module 2
Page 15 and 16: module. At the end of this section
Page 17 and 18: there are set up search engines. Af
Page 19 and 20: UTILITY CLASSES: AgentController -
Page 21 and 22: ) If initial step of the algorithm
Page 23 and 24: 2.3 Design of the database part Thi
Page 25 and 26: Table search_engine: This table con
Page 27 and 28: 3. Algorithms This part of the thes
Page 29 and 30: Example: Let us consider the follow
Page 31: During algorithm processing there i
Page 35 and 36: Example: Consider the following ini
Page 37 and 38: Listing 3.2.3 presents the pseudo c
Page 39 and 40: 3.3 Consensus method The Consensus
Page 41 and 42: Depending on the outcome of the con
Page 43 and 44: 3.4 Common algorithms This part of
Page 45 and 46: is multiplied by 0.01. 3 If the alg
Page 47 and 48: Input: Two lists with URLs Output:
Page 49 and 50: 4.1 Tests with simple query Followi
Page 51 and 52: top enough positions in all result
Page 53 and 54: The following section presents resu
Page 55 and 56: This part will present comparison o
Page 57 and 58: As the first URL MySpiders returns
Page 59 and 60: was yield. However most of the retu
Page 61 and 62: The following part presents compari
Page 63 and 64: The following part presents compari
Page 65 and 66: complexity of the query. As the fir
Page 67 and 68: 7 http://www.calresco.org/lucas/pmo
Page 69 and 70: 5 6 http://www.virtualschool.edu/mo
Page 71 and 72: 10 http://www.mises.org/story/2451
Page 73 and 74: Auction Consensus Game theory Aucti
Page 75 and 76: 5. Final remarks In this thesis app
Page 77 and 78: [15] Kessler R.R., Griss M.L., Maki
Page 79: Warszawa, dnia 21.04.2008r. Oświad

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