Self-optimising CBR retrieval

被引：23

作者：

Jarmulak, J ^{[1
]}

Craw, S ^{[1
]}

Rowe, R ^{[1
]}

机构：

[1] Robert Gordon Univ, Sch Comp & Math Sci, Aberdeen AB25 1HG, Scotland

来源：

12TH IEEE INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, PROCEEDINGS | 2000年

关键词：

D O I：

10.1109/TAI.2000.889897

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

One reason why Case-Based Reasoning (CBR) has become popular is because it reduces development cost compared to rule-based expert systems. Still, the knowledge engineering effort may be demanding. In this paper we present a tool which helps to reduce the knowledge acquisition effort for building a typical CBR retrieval stage consisting of a decision-tree index and similarity measure. We use Genetic Algorithms to determine the relevance/importance of case features and to find optimal retrieval parameters. The optimisation is done using the data contained in the case-base. Because no (or little) other knowledge is needed this results in a self-optimising CBR retrieval. To illustrate this we present how the tool has been applied to optimise retrieval for a tablet formulation problem.

引用

页码：376 / 383

页数：8

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