Rule-based errors

In order to cope with a complex environment, people make extensive use of rules or assumptions. This rule based mode of operation is normally very efficient. However, errors will arise when the underlying assumptions required by the rules are not fulfilled. Chapter 2 discusses the causes of these rule based errors in detail. 

Rule-based errors are a consequence of if this, then that action rules. Errors of this type may occur when there is insufficient training or currency with the operating environment. The mle may be inappropriately applied to the given situation. 

Work procedures Studies have showed that 11 to 24% industrial accidents occurred due to lack and weaknesses related in procedures (Peterson 1996). Rasmussen s classified these errors as rule based errors (Kirwan 1997). In this study, some of human errors identified in the PHEA worksheets, were because of shortcomings in... 

The classification of human error within the FASGEP project takes into account the woric of Rasmussen, specifically the classification of Rule-Based errors, Skill-Based errors, and Knowledge-Based errors. The causal relationships for each of the classifications have been developed into a causal network. A type of graphical probability model is based on this. 

Reason goes further and highlights the likely feilure modes at each level of behaviour. For exanq>le skill-based enors occur due to control-mode fiulures of both inattention and over-attention. Rule-based errors can arise fiom the misapplication of rules or the q>plication of incorrect rules. Knowledge-based errors can arise due to selectivity, biased reviewing and a number of other fiictors. 

Rule-based errors are concerned with the misapplication or inappropriate use of problem solving rules. Individuals have a complex array of specific and general rules that they use to deal with everyday problems. Rules are of the type i/ then . Some simplistic examples relating to the operation of vehicles are ... 

The same study showed that rule-based errors were no more frequent when the task was more complex and that these are not detected significantly more effectively by those with more experience. Rare errors of knowledge, however, are detected much better by expert subjects. This work provides spectacular confirmation of the complexity of managing cognitive compromises. 

With regard to mistakes, two separate mechanisms operate. In the rule-based mode, an error of intention can arise if an incorrect diagnostic rule is used. For example, a worker who has considerable experience in operating a batch reactor may have learned diagnostic rules that are inappropriate for continuous process operations. If he or she attempts to apply these rules to evaluate the cause of a continuous process disturbance, a misdiagnosis could result, which could then lead to an inappropriate action. In other situations, there is a tendency to overuse diagnostic rules that have been successful in the past. 

The lighter arrows represent typical shortcuts, which omit particular stages in the information-processing chain. These shortcuts may be "legitimate," and would only lead to errors in certain cases. For example, the worker may erroneously believe that he or she recognizes a pattern of indicators and may immediately execute a skill-based response, instead of moving to the rule-based level to apply an explicit diagnostic rule. 

The practical implications of this experiment are that when evaluating the effects of shift work due to circadian effects, the type of task being carried out by the worker must be taken into account. For example, skill-based tasks would be expected to exhibit the performance changes characteristic of low memory load tasks, whereas performance variations in knowledge-based tasks would be expected to follow the pattern of high memory load tasks. Performance on rule-based tasks may depend on the degree of frequency of use of the rules, which in turn may determine the memory load. If these results were confirmed by further process plant studies, it would have implications for when different types of operation (involving different levels of memory load) should be scheduled to reduce circadian rhythm effects and minimize errors. 

Rules seemingly have the same format as IF.. THEN.. statements in any other conventional computer language. The major difference is that the latter statements are constructed to be executed sequentially and always in the same order, whereas expert system rules are meant as little independent pieces of knowledge. It is the task of the inference engine to recognize the applicable rules. This may be different in different situations. There is no preset order in which the rules must be executed. Clarity of the rule base is an essential characteristic because it must be possible to control and follow the system on reasoning errors. The structuring of rules into rule sets favours comprehensibility and allows a more efficient consultation of the system. Because of the natural resemblance to real expertise, rule-based expert systems are the most popular. Many of the earlier developed systems are pure rule-based systems. 

Second-order and third-order results often bracket the true correction to pF - Three schemes that scale the third-order terms in various ways are known as the Outer Valence Green s Function (OVGF) [8], In OVGF calculations, one of these three recipes is chosen as the recommended one according to rules based on numerical criteria. These criteria involve quantities that are derived from ratios of various constituent terms of the self-energy matrix elements. Average absolute errors for closed-shell molecules are somewhat larger than for P3 [31]. 

The properties of slags are dependent not only upon chemical composition, but upon other factors also. The most pronounced deviations from additivity rules based on composition arise in the estimation of those properties which involve ionic transport eg. electrical conductivity. However surface tension values estimated from additivity rules are frequently in error as bulk thermodynamic properties do not apply at surfaces. Furthermore, virtually all the physical properties of slags are, to some extent, dependent upon the structure of the slag (viz. the length of silicate chains, degree of crystallinity etc.) thus estimation procedures have to accommodate these structural factors, where possible. 

D is the Debye-Huckel term in molal units and / , the ionic strength converted to molal units by using the conversion factors listed in [76BAE/MES] (p.439). The following list gives the details of this calculation. The resulting uncertainties in log p are obtained based on the rules of error propagation as described in Section C.6.2. 

The ligand molecules are taken from a compound databases typically containing only structural formulae of the individual molecules. In order to use them in a docking calculation, 3D structures of the molecules have be generated. For this step, very powerful tools like Concord [166] or Corina [68, 69] exist. In addition, atom hybridization and protonation as well as bond types have to be determined. Typically, this task is performed by a series of subsequently executed rule-based scripts. It should be noted that due to the complexity of this task, the resulting structures are not free of errors in the assignment of atom and bond types. The docking software should therefore rely as little as possible on this information. 

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