Edwards M, Morse D R, Fielding A H
Department of Biological Sciences, Manchester Polytechnic, UK.
Comput Appl Biosci. 1987 Mar;3(1):1-7. doi: 10.1093/bioinformatics/3.1.1.
The role of expert systems in species identification, with particular reference to the problems posed by damaged specimens and inexperienced taxonomists, is discussed. Of the three main types of expert systems available, the frame-based system is shown to provide the most appropriate model for a taxonomic expert system rather than a logic- or rule-based system. The advantages of an expert system over other computer-aided methods of identification are considered. A rule-based system requires the original knowledge (species descriptions) to be structured into rules, whereas a frame-based system can store the generic and specific descriptions in a series of frames. The frames fall into a hierarchy which closely resembles the taxonomic hierarchy, and down which information can be inherited. Two aspects of frame-based systems considered are the use of probabilities in identification, and the optimum structure of the knowledge base. The conventional use of probabilities is to provide an indication of the correctness of the result. However, in some studies involving the identification of many specimens, the speed of identification may be increased (with a reduction in accuracy) if identifications are made to a predetermined probability level. Although frames allow accurate representation of the taxonomic hierarchy, a semantic net, incorporating structures of the organism and/or details of the habitat may result in a more efficient expert system.
本文讨论了专家系统在物种识别中的作用，尤其关注受损标本和缺乏经验的分类学家所带来的问题。在现有的三种主要类型的专家系统中，基于框架的系统被证明为分类学专家系统提供了最合适的模型，而非基于逻辑或规则的系统。文中还探讨了专家系统相较于其他计算机辅助识别方法的优势。基于规则的系统要求将原始知识（物种描述）构建成规则，而基于框架的系统可以将一般和具体描述存储在一系列框架中。这些框架形成一个层次结构，与分类学层次结构极为相似，信息可沿此层次结构继承。文中考虑了基于框架系统的两个方面，即识别中概率的使用以及知识库的最佳结构。概率的传统用途是指示结果的正确性。然而，在一些涉及大量标本识别的研究中，如果将识别设定在预定的概率水平，识别速度可能会提高（但准确性会降低）。尽管框架能够准确表示分类学层次结构，但结合生物体结构和/或栖息地细节的语义网络可能会产生更高效的专家系统。
