Advances in Artificial Intelligence: International Joint by Sholom M. Weiss, Nitin Indurkhya (auth.), Maria Carolina

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By Sholom M. Weiss, Nitin Indurkhya (auth.), Maria Carolina Monard, Jaime Simão Sichman (eds.)

This 12 months, Brazil celebrates its 500 years of discovery. To mark this nice occasion, the Brazilian Arti cial Intelligence (AI) neighborhood prepared a distinct int- nationwide joint convention placing jointly SBIA 2000 (the Brazilian AI Sym- sium) and IBERAMIA 2000 (the Ibero-American AI Conference). SBIA 2000 is the fifteenth convention of the SBIA convention sequence, that's the best convention in Brazil for presentation of analysis and functions in Arti cial Intelligence. when you consider that 1995, SBIA has develop into a world convention, with papers written in English, a global software committee, and p- ceedings released in Springer-Verlag’s Lecture Notes in Arti cial Intelligence (LNAI) sequence. IBERAMIA 2000 is the seventh convention of the IBERAMIA convention sequence, which has been probably the most appropriate boards for ibero-american AI rese- chers (from South and critical the United States, Mexico, Spain, and Portugal) to offer their effects. Following the SBIA and EPIA (Portuguese convention on AI) ex- riences, from IBERAMIA’98 on, it has additionally develop into a world convention, with court cases released in Springer-Verlag’s LNAI series.

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Additional resources for Advances in Artificial Intelligence: International Joint Conference 7th Ibero-American Conference on AI 15th Brazilian Symposium on AI IBERAMIA-SBIA 2000 Atibaia, SP, Brazil, November 19–22, 2000 Proceedings

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De Andrade Lopes and A. Jorge Coverage x Error 0,25 0,2 CBR 0,15 Error 0,1 RC1 RC2 0,05 0 50 60 70 80 90 100 Coverage % Fig. 1. Coverage ™ Error. The first four iterations use the CSC(RC1) algorithm and in the last one we use the algorithms CBR, RC1, and RC2. Figure 1 shows the coverage vs.

PSEC007, which is related to a different task, is not further considered. Then, for all potential reuse candidates a similarity value is computed by partially matching the indexes (except the ones marked as essential) using a tailored similarity measure wrt. the retrieval goal. Cases with a higher similarity value than a given threshold are considered as sufficiently similar and proposed to the user as reuse candidates ranked by their similarity values. Continuing the example shown in Table 1, case PSEC003 is considered more similar to the given situation than PSEC011, because the values of the indexes of PSEC003 marked as important or less important are more similar to the current ones.

For the experiments described in this article, the typical number of iterations was 5.  The iterative induction strategy employs a kind of covering mechanism. Given a first order language L, examples E (tagged words) and background knowledge BK, it proceeds as follows. First, all the examples are used to generate a large set of rules in language L. From those rules, we select a subset of the ones with satisfying quality above a given threshold of rule confidence. The confidence of a rule is the proportion of correct answers it gives.

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