Expert systems as a tool for statistics: A review

The current state of using expert system (ES) techniques for statistics is expounded through the difficulties of defining the notion of statistical expert system (SES) and through several well-known examples. The main characteristics of statistical expertise, and particularly the important concept of strategy are emphasized. The current state of SESs is seen in the context of the global trend of the evolution of ES, i.e. within the growth of second generation expert system (SGESs). The knowledge base of these ESs represent an attempt to represent not only experts' knowledge but also the way experts acquire their expertise, thanks to the existence of an underlying model. We show through many examples how far SESs are and/or will be able to be SGESs.     

Schedule-an expert-like system for machine scheduling

The construction of an expert-like system for machine scheduling called SCHEDULE is presented. Essential parts of SCHEDULE were developed by students in a laboratory course Operations Research on Microcomputers at the University of Karlsruhe, Germany. SCHEDULE consists of the components data base, knowledge base, inference engine, explanation facility, dialog component, and knowledge acquisition component. The knowledge base contains an algorithm base for solving different types of scheduling problems. To establish the rules of the knowledge base the well-known three-field classification of deterministic machine scheduling problems and the concept of the reduction digraph are exploited. Experiences gained during building and demonstrating SCHEDULE are reported.     

Flow systems exploiting in-line prior assays

An expert sequential injection system involving a prior assay is proposed for spectrophotometric determination of phosphate and eventually zinc in soil extracts. The result of phosphate determination is the basis for a concentration-oriented decision regarding to the need or not for zinc determination. Zinc was only determined if a threshold value (peak height corresponding to 5.0 mg l⁻¹ P) was surpassed. The methods involved formation of molybdenum blue and the Rhodamine 6G/ammonium thiocyanate/Zn²⁺ ternary complex. Variations in the threshold value were < 2% during 4 h operating periods, false responses were not verified, and the analytical time was reduced in about 30%. Precise results (R.S.D. <3% P and < 1% Zn) in agreement with spectrophotometry and flame atomic absorption spectrometry were obtained. The innovation permits faster information processing, as well as a reduction in the number of measurements, number of analytical steps, laboratorial time, and consumption of sample and reagents, thus waste generation.     

液相色谱专家系统中柱系统推荐规则的建立和验证

把分子结构和分子、离子静电作用力作为影响溶质保留值和选择性的基本点,提出了液相色谱专家系统柱系统推荐的基本规则,由此可以从溶质所包含的基团或官能团的信息进行色谱柱系统推荐。为了验证这些规则的正确性,应用几类常用的或典型的样品从分子结构通道进行液相色谱柱系统推荐,获得满意的结果。     

Retrieving definitional content for ontology development

Ontology construction requires an understanding of the meaning and usage of its encoded concepts. While definitions found in dictionaries or glossaries may be adequate for many concepts, the actual usage in expert writing could be a better source of information for many others. The goal of this paper is to describe an automated procedure for finding definitional content in expert writing. The approach uses machine learning on phrasal features to learn when sentences in a book contain definitional content, as determined by their similarity to glossary definitions provided in the same book. The end result is not a concise definition of a given concept, but for each sentence, a predicted probability that it contains information relevant to a definition. The approach is evaluated automatically for terms with explicit definitions, and manually for terms with no available definition.     

New tools: Expert systems for uncertainty budgets

An important part of quality assurance in any analytical laboratory is the production of comprehensive results integrating uncertainty measurements. Many testing laboratories face the problem that the expenditure required to evaluate small uncertainties (high precision and high accuracy) is often uneconomic. In most cases an uncertainty of high reliability has to be calculated from only a few data (one calibration, few replications, etc.). This problem can be solved by an expert system. To achieve this the analytical procedure has to be structured into a dialouge and divided into parts. The uncertainty has to be calculated for each part of the procedure. Addition of the individual uncertainties results in the combined and expanded uncertainty. During the dialouge the system should advise the analyst how to get an efficient and effective calculation of uncertainty. All calculations, mathematical and statistical procedures have to be surveyable but running the system should not be too time consuming for economic reasons. Within the scope of the EURECA-project initiated by the Eidgenössische Materialprüfungs- und Forschungsanstalt (EMPA), St. Gallen, Switzerland, expert system software is being developed in cooperation with other research institutes and manufacturers of analytical instruments. Using this software it will be possible to calculate the uncertainty for analytical procedures such as titration, atomic emission spectrometry (ICP-OES), atomic absorption spectrometry (AAS) and gas- and liquid chromatography (GC, HPLC).     

A genetic algorithm for the automated generation of molecules within constraints

Summary A genetic algorithm has been designed which generates molecular structures within constraints. The constraints may be any useful function, for example an enzyme active site, a pharmacophore or molecular properties from pattern recognition or rule-induction analyses. The starting point may be random or may utilise known molecules. These are modified to grow into families of structures which, using the evolutionary operators of selection, crossover and mutation evolve to better fit the constraints. The basis of the algorithm is described together with some applications in lead generation, 3D database construction and drug design. Genetic algorithms of this type may have wider applications in chemistry, for example in the design and optimisation of new polymers, materials (e.g. superconducting materials) or synthetic enzymes.     

QSAR Modeling of Large Heterogeneous Sets of Molecules

Abstract

In aquatic toxicology, QSAR models are generally designed for chemicals presenting the same mode of toxic action. Their proper use provides good simulation results. Problems arise when the mechanism of toxicity of a chemical is not clearly identified. Indeed, in that case, the inappropriate application of a specific QSAR model can lead to a dramatic error in the toxicity estimation. With the advent of powerful computers and easy access to them, and the introduction of soft modeling and artificial intelligence in SAR and QSAR, radically different models, designed from large non-congeneric sets of chemicals have been proposed. Some of these new QSAR models are reviewed and their originality, advantages, and limitations are stressed.     

磺酰脲类除草剂的三维药效团模型

磺酰脲类除草剂是七十年代末期[1，2]开发的一类超高效除草剂，杀草谱广，用量极低．它通过抑制植物体内乙酰乳酸合成酶（ALS，acetolactatesynthase）的活性[3]，破坏支链氨基酸缬氨酸、亮氨酸和异亮氨酸的合成，造成杂草的死亡．本文从三维角度出发，利用先进的三维分子设计软件APEX（ActivityPredictionExpertSystem）－3D[4]，建立破酸豚类除草剂药效团模型（biophoremodel），辅助设计、筛选活性化合物；再由药效团构造出模拟ALS酶活性位点（activesites）的空间结构，以该模拟结构为基点，以期设计出全新结构的ALS酶抑制剂的…     

Jackknife evaluation of uncertainty judgments aggregated by the Kullback-Leibler distance

One feasible approach to aggregating uncertainty judgments in risk assessments is to use calibration variables (or seed questions) and the Kullback-Leibler (K-L) distance to evaluate experts’ substantive or normative expertise and assign weights based on the corresponding scores. However, the reliability of this aggregation model and the effects of the number of seed questions or experts on the stability of the aggregated results are still at issue. To assess the stability of the aggregation model, this study applies the jackknife re-sampling technique to a large data set of real-world expert opinions. We also use a nonlinear regression model to analyze and interpret the resulting jackknife estimates. Our statistical model indicates that the stability of Cooke’s classical model, in which the components of the scoring rule are determined by the K-L distance, increases exponentially as the number of seed questions increases. Considering the difficulty and importance of creating and choosing appropriate seed variables, the results of this study justify the use of the K-L distance to determine and aggregate better probability interval or distribution estimates.