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With expanding reputation of agent-based computing, loads of new study concerning the identity and definition of compatible types, instruments, and methods to aid the advance of advanced Multiagent structures (MAS) has emerged. This learn, quite often pointed out as Agent-Oriented software program Engineering (AOSE), consistently proposes new metaphors, new formal modeling methods and methods, and new improvement methodologies and instruments.
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The most well-known type of MS is Kurchan square posed by Rodolfo Kurchan (1989), which is originated from magic square. The maximum product minus the minimum one is as small as possible in a Kurchan Square. However, in the MAX version of MS, sum of the following products is maximum. The score function of a MS of dimension 3 is illustrated in Fig. 1. Rows: 5*1*8 = 40, 3*9*4 = 108, 7*2*6 = 84 Columns: 5*3*7 = 105, 1*9*2 = 18, 8*4*6 = 192 Diagonals: 5*9*6 = 270, 1*4*7 = 28, 8*3*2 = 48 Anti-diagonals: 8*9*7 = 504, 1*3*6 = 18, 5*4*2 = 40 MAXMS: SF= 40+108+84+105+18+192+270+28+48+504+18+40= 1455 Kurchan MS: SF= 504-18 = 486 5 3 7 1 9 2 8 4 6 Fig.
In this paper we use the web, the most comprehensive text corpus, in order to improve the low values of recall. Therefore, we use the search engines for searching web pages. Two information elements provided by search engines are the number of pages retrieved for the query Q and an abstraction of the document that contains the concept. In this paper we use these two elements for extracting ‘is-a’ relations. Another group of methods for extracting ‘is-a’ relations is based on distribution hypothesis .
Bayesian network provides a compact representation or factorization of the joint probability distribution for a group of variables . In this way, the joint probability distribution for the entire network can be specified. This relationship can be captured mathematically using the chain rule in Equation 1 . n p( x) = ∏ p( xi | parents( xi )) (1) i =1 We are interested in learning BN from training data D consisting of examples x. The two major tasks in learning a BN are: learning the graphical structure, and then learning the parameters (CP table entries) for that structure.