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 equipped a unique 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 prime convention in Brazil for presentation of study and purposes in Arti cial Intelligence. considering the fact that 1995, SBIA has turn 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 essentially the most compatible boards for ibero-american AI rese- chers (from South and crucial the USA, Mexico, Spain, and Portugal) to provide their effects. Following the SBIA and EPIA (Portuguese convention on AI) ex- riences, from IBERAMIA’98 on, it has additionally turn into a global convention, with court cases released in Springer-Verlag’s LNAI series.
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Extra info 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
On the other hand, semantic methods entail some kind of case understanding or case explanation and employ background knowledge. Case explanations are used to retrieve and adapt cases. For this work, we adopted this second view of cases. In early experiments with syntactic methods a case was represented as a set of features corresponding to a neighborhood of the word to tag of length 11. The overlapping metric used divided the number of matching features by the total number of features representing the context of the word.
However, if multiple retrieval goals have to be supported by a case base, this is not sufficient. The creation of distinct case bases for test selection and diagnosis in PATDEX , can be seen in analogy to different retrieval goals, although inefficient due to administration and maintenance reasons. In contrast, our approach, systematizes the concept of goal-oriented retrieval through a flexible and tailorable retrieval method and similarity measure based on the advanced similarity model of PATDEX which explicitly deals with unknown information, filter attributes, and local similarity measures.
In addition, consider the optimal choice as B, and the result presented by the algorithm as A. For example, there are, as optimal choices, the set B and the set B , where B ∪ B = M. Where B is a set of remained cases by the deletion of B from M. The same with A and A , resulted by the algorithm. For a coverage of a set X of cases, the notation is XC = Coverage(X). Intuitively, the lost coverage is the total coverage N(M) minus the coverage remaining. Thus, it can be defined as: (5) X LC = LostCoverage( X ,M) = |N(M) – N(X)| .
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 by Sholom M. Weiss, Nitin Indurkhya (auth.), Maria Carolina Monard, Jaime Simão Sichman (eds.)