Rule based performance

Rule-based At the middle level, these are performance tasks that involve actions governed by training and procedures that are executed consistently by different operators across a facility. These typically correspond to written procedures learned in operations and maintenance classrooms and on-the-job training. An example of a mistake associated with rule-based performance is the omission of a step in a formal written procedure. 

Performance is said to be at either a skill-based or a rule-based level when tasks are routine in nature. Skill-based performance involves the smooth, automatic flow of actions without conscious decision points. As such, skill-based performance describes the decisions made by highly trained operators performing familiar tasks. Rule-based performance involves the conscious perception of environmental cues, which trigger the application of rules learned on the basis of experience. As such, rule-based performance corresponds closely to recognition-primed decisions (Klein 1989). The... 

Rule-based performance, 2205, 2206 Rule orientation (as national culture dimension), 957, 960 Rules of order (for groups), 2213 Rules ... 

Rule based performance - behaviours which require more mental effort and planning because we are operating in less famiUar territory or where a skill-based approach isn t going to work. 

Rule-based (performance of less familiar tasks according to remembered or defined rules or estab-hshed procedures),... 

Rule-based performance is called the rule-based level because people apply memorized orwritten rules. These rules may have been learned as a result of interaction with the facility, through formal training or by working with experienced workers. 

Rule-based performance requires following preset solutions then taking prearranged specific actions. This performance uses an If-Then decision-making process. If X occurs, then Y is required (US DOE, 2009a). The JHA defines scenarios where standard operating procedures, rules, guidelines, and structure are essential. Errors can possibly occur when rules or protocols are misinterpreted or established procedures not followed or understood. 

The development vehicle used to create and test the rule base must be as flexible as possible, allowing easy alterations and expansion of the rule base with whatever displays can convey the most information. The delivery vehicle, however, should be virtually transparent to the user, conveying only as much information as needed to solve the problem at hand. Self-tuning controllers can perform their task without explicitlv informing users, but their output and status is available on demanci, and their operation may be easily limited or interrupted. 

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. 

SpinPro is a typical backward chaining, rule-based expert system. Rule-based systems are systems in which the expert s knowledge is encoded primarily in the form of if-then rules, i.e., if a set of conditions are found to be true then draw a conclusion or perform an action. "Backward chaining" refers to the procedure for finding a solution to a problem. In a backward chaining system, the inference engine works backwards from a hypothesized solution to find facts that support the hypothesis. Alternative hypotheses are tried until one is found that is supported by the facts. 

This point is illustrated by a second example. A vapor-phase spectrum of propionitrile was obtained and its digitization is shown in Table III. For the sake of example, assume the scientist entered the 2246 cm peak as average rather than sharp. The interpretation would result in likelihoods of 0.90 for isocyanate and 0.30 for nitrile. Performing the interpretation with the tracing function turned on would quickly show that the rules base the distinction between isocyanate and propionitrile very heavily on the width of the peak in the vicinity of 2260 cm . Reinterpreting this spectrum with the correct, sharp width entered for the 2246 cm peak results in a nitrile likelihood of 0.50 and isocyanate of 0.40. 

An example of the cure cycle optimization is the work of Thomas et al. who used a very sophisticated model, together with a rule-based optimization routine, to pick the shortest cure cycle that met a set of performance criteria [15]. Reductions in cure time using this method ranged up to 36 percent for a single complex part and from 8 percent up to 43 percent for batches of mixed parts. Rejection rates were not increased in any case, and they were actually reduced significantly for one part. This model, although transferred to a number of companies, unfortunately has had limited use because of the lack of support for the code and the cost of qualifying it on new materials. 

---