The effects of heuristic rule training on operator performance in a simulated process control environment

In complex work environments, the occurrence of novel system states represents a particular challenge for the design of training. This article is concerned with the use of heuristic rules to prepare operators for the management of unfamiliar fault states. An experiment was carried out to examine the effects of heuristic rule training on operator performance and system management behaviour. Thirty-nine trainee operators from the chemical industry took part in the study. They were trained for 4 h on a PC-based simulation of a process control task. Operators in the experimental group received training on heuristic rules while operators in the control group did not. One week later the operators participated in a 70-min testing session. While the results showed that heuristic rules training led to better diagnostic performance, it was also associated with increased operator fatigue and impaired secondary task performance. The implications of the findings for using heuristic rule training are discussed.     

Mining the fuzzy control rules of aeration in a Submerged Biofilm Wastewater Treatment Process

This paper presents a special rule base extraction analysis for optimal design of an integrated neural-fuzzy process controller using an “impact assessment approach.” It sheds light on how to avoid some unreasonable fuzzy control rules by screening inappropriate fuzzy operators and reducing over fitting issues simultaneously when tuning parameter values for these prescribed fuzzy control rules. To mitigate the design efforts, the self-learning ability embedded in the neural networks model was emphasized for improving the rule extraction performance. An aeration unit in an Aerated Submerged Biofilm Wastewater Treatment Process (ASBWTP) was picked up to support the derivation of a solid fuzzy control rule base. Four different fuzzy operators were compared against one other in terms of their actual performance of automated knowledge acquisition in the system based on a partial or full rule base prescribed. Research findings suggest that using bounded difference fuzzy operator (O_b) in connection with back propagation neural networks (BPN) algorithm would be the best choice to build up this feedforward fuzzy controller design.     

The effect of time pressure on expert system based training for emergency management

In many emergency situations, human operators are required to derive countermeasures based on contingency rules whilst under time pressure. In order to contribute to the human success in playing such a role, the present study intends to examine the effectiveness of using expert systems to train for the time-constrained decision domain. Emergency management of chemical spills was selected to exemplify the rule-based decision task. An Expert System in this domain was developed to serve as the training tool. Forty subjects participated in an experiment in which a computerized information board was used to capture subjects' rule-based performance under the manipulation of time pressure and training. The experiment results indicate that people adapt to time pressure by accelerating their processing of rules where the heuristic of cognitive availability was employed. The simplifying strategy was found to be the source of human error that resulted in undesired decision performance. The results also show that the decision behaviour of individuals who undergo the expert system training is directed to a normative and expeditious pattern, which leads to an improved level of decision accuracy. Implications of these findings are examined in the present study.     

The effect of time pressure on expert system based training for emergency management

In many emergency situations, human operators are required to derive countermeasures based on contingency rules whilst under time pressure. In order to contribute to the human success in playing such a role, the present study intends to examine the effectiveness of using expert systems to train for the time-constrained decision domain. Emergency management of chemical spills was selected to exemplify the rule-based decision task. An Expert System in this domain was developed to serve as the training tool. Forty subjects participated in an experiment in which a computerized information board was used to capture subjects' rule-based performance under the manipulation of time pressure and training. The experiment results indicate that people adapt to time pressure by accelerating their processing of rules where the heuristic of cognitive availability was employed. The simplifying strategy was found to be the source of human error that resulted in undesired decision performance. The results also show that the decision behaviour of individuals who undergo the expert system training is directed to a normative and expeditious pattern, which leads to an improved level of decision accuracy. Implications of these findings are examined in the present study.     

Use of genetic algorithms in training diagnostic rules for process fault diagnosis

The self learning of diagnostic rules can ease knowledge-acquisition effort, and it is more desirable in cases where experience about certain faults is not available. Applications of genetic algorithms to the self learning of diagnostic rules for a pilot-scale mixing process and a continuous stirred-tank reactor system are described in the paper. In this method, a set of training data, which could be obtained from simulations and/or from the recorded data of the previous operations of the real process, is required. The training data is divided into various groups corresponding to various faults and the normal operating condition. Corresponding to each fault, there is a group of initial rules which are coded into binary strings. These rules are evaluated by a genetic algorithm which contains the three basic operators, reproduction, crossover and mutation, and an added operator which preserves the best rule ever discovered. Through this biological-type evaluation, new fitted rules are discovered. The results demonstrate that diagnostic rules fitted with a given set of training data can be efficiently discovered through genetic learning, and, hence, that genetic algorithms provide a means for the automatic creation of rules from a set of training data. It is also demonstrated that bad training data and the inappropriate formulation of rules could degrade the performance of the learning system.     

Impact of automated decision aids on performance,operator behaviour and workload in a simulated supervisory control task

In studies reporting automation effects on overall system performance and on the operator, the methods used to measure workload often did not appropriately reflect the complexity of this construct. The present study addresses the impact of automation on operator workload and behaviour in process control fault management. Workload effects were assessed with subjective, cardiovascular and secondary task performance indicators. Interactions with the interface of the process control simulation directed at gathering information and controlling the system were recorded. Automation made operators more efficient, allowing faster fault management with less information sampling and control actions. Subjective workload ratings were significantly lower in the assisted conditions as compared to manual, which was not reflected in cardiovascular and secondary task measures. Participants' information sampling activity did not differ between medium and high level of automation. Results suggest that participants paid constantly high attention to their task even with highly automated support.     

The predictive qualities of operator characteristics for process control performance: the influence of personality and cognitive variables

This article examines the relationship between operator characteristics and process control performance. Thirty-nine trainee operators participated in a 4-h training session of a simulated process control task and a testing session in which various system faults had to be managed. Cognitive ability, cognitive flexibility, self-efficacy and personality traits were measured as operator characteristics. Cognitive ability related positively to system control performance but not to diagnostic performance. Participants with low cognitive flexibility performed best on system control, whereas participants with high cognitive flexibility performed best on diagnostic performance. A hierarchical regression analysis revealed that cognitive ability, cognitive flexibility and declarative knowledge accounted for about 30% of the variability of system control. The findings suggest that consideration of cognitive ability and cognitive flexibility be increased in personnel selection for complex work environments.     

Binary classification rule generation from decomposed data

Learning classification rules from data that do not fit in the available memory is a challenging task. The goal of this study is to develop an approach for generating binary classification rules from decomposed data that are equivalent in terms of quality to those found over the whole data. In the proposed approach, each class is divided into the same arbitrary small number of subtables. For each pair of subsets from different classes, rule sets are induced using any sequential covering algorithm. Rule sets generated from the same positive class subset and different negative class subsets are merged using an operator constructed on the basis of Cartesian product and conjunction operators. The rule sets obtained in this way are joined into one set. During the rule merging, unnecessary rules are removed. It is proven that for training data, the quality of the rule set generated using the approach is the same as that for the whole data. It is experimentally verified that for test data, the quality of classification is comparable with that obtained using a nondecomposed data approach.     

Fuzzy rule generation for adaptive scheduling in a dynamic manufacturing environment

This paper proposes a fuzzy rule-based system for an adaptive scheduling, which dynamically selects and applies the most suitable strategy according to the current state of the scheduling environment. The adaptive scheduling problem is generally considered as a classification task since the performance of the adaptive scheduling system depends on the effectiveness of the mapping knowledge between system states and the best rules for the states. A rule base for this mapping is built and evolved by the proposed fuzzy dynamic learning classifier based on the training data cumulated by a simulation method. Distributed fuzzy sets approach, which uses multiple fuzzy numbers simultaneously, is adopted to recognize the system states. The developed fuzzy rules may readily be interpreted, adopted and, when necessary, modified by human experts. An application of the proposed method to a job-dispatching problem in a hypothetical flexible manufacturing system (FMS) shows that the method can develop more effective and robust rules than the traditional job-dispatching rules and a neural network approach.     

多阶段多产品调度问题的链式智能体遗传算法

将遗传算法的编码方式与智能体系统的演化结构相结合,提出一种求解多阶段多产品调度问题的链式智能体遗传算法.算法采用基于订单序列的编码方式,采用一种新的后向指派规则实现编码和可行调度间的一一对应.通过各智能体与其邻域环境的竞争与合作以及自身的自学习操作实现种群的演化过程.对多阶段多产品调度问题的仿真结果表明:链式智能体遗传...     

多分类器系统的泛组合规则研究与应用

现有的多分类器系统采用固定的组合算子,适用性较差。将泛逻辑的柔性化思想引入多分类器系统中,应用泛组合运算模型建立了泛组合规则。泛组合规则采用遗传算法进行参数估计,对并行结构的多分类器系统具有良好的适用性。在时间序列数据集上的分类实验结果表明,泛组合规则的分类性能优于乘积规则、均值规则、中值规则、最大规则、最小规则、投票规则等固定组合规则。     

The impact of knowledge representation on cognitive‐oriented task performance

This research examines the impact of training style and operator individual differences on the task representation developed, automatized task performance, and controlled task performance. Results indicate that performance on relatively straightforward repetitive tasks usually associated with automatization is influenced by training style and the mental task representation held by operators. Also, domain representation is a significant determinant of performance on complex cognitive‐oriented tasks requiring controlled processes. Therefore, the task representation is identified as a high‐level performance determinant for both simple and complex task performance. No effect for training style or individual differences was found. It is concluded that training programs for systems requiring human‐computer interaction must account for this factor in order to facilitate the learning process and enhance task performance.     

Secure Mobility and the Autonomous Driver

Autonomous mobility systems developed for unmanned ground vehicles may have additional benefits by enhancing system performance and reducing demands on operators for manned ground vehicles. This effort examines the potential impact of introducing autonomous mobility to control manned vehicles while operators performed secure mobility. Eleven Soldiers participated in an experimental task requiring concurrent control of a manned and an unmanned Stryker performing a road march, scanning of the local environment for targets, and planning of a reconnaissance route for a third simulated asset. The control of the manned vehicle was varied between autonomous and manual control and several speed and accuracy variables were examined for each task. Subjective measures of operator workload, stress, and motion sickness were also examined. The results support the potential benefits of incorporating autonomous mobility into manned platforms. In speed-matched conditions, autonomous mobility was associated with decreased manned vehicle mission time, faster operator reaction times to targets, greater instances of multitasking while under motion, and lower subjective operator workload measures than with manual driving. In conclusion, autonomous mobility technologies have the potential to free up resources from the vehicle operator and allow for better operator performance on tasks other than vehicle control.     

Effects of training on short- and long-term skill retention in a complex multiple-task environment

The paper reports the results of an experiment on the performance and retention of a complex task. This was a computer-based simulation of the essential elements of a spacecraft's life support system. It allowed the authors to take a range of measures, including primary and secondary task performance, system intervention and information sampling strategies, mental model structure, and subjective operator state. The study compared the effectiveness of two methods of training, based on low level (procedure-based) and high level (system-based) understanding. Twenty-five participants were trained extensively on the task, then given a 1-h testing session. A second testing session was carried out 8 months after the first (with no intervening practice) with 17 of the original participants. While training had little effect on control performance, there were considerable effects on system management strategies, as well as in structure of operator's mental model. In the second testing session, the anticipated general performance decrement did not occur, though for complex faults there was an increase in selectivity towards the primary control task. The relevance of the findings for training and skill retention in real work environments is discussed in the context of a model of compensatory control.     

Comparative study of three training methods for enhancing process control performance: Emphasis shift training,situation awareness training,and drill and practice

Three training methods to improve attention management skills in process control were compared. Forty students from technical disciplines participated in a five-hour module of emphasis shift training (EST), EST combined with situation awareness training (EST/SA), and drill and practice (D&P) on a simulated process control task. Participants were then tested three times for 45 min each (immediately after training, two weeks after training, and six weeks after training) for system control performance and diagnostic performance on familiar and nonfamiliar fault states. D&P led to superior diagnostic performance on familiar system faults. EST/SA training supported the diagnosis of novel system faults. EST was less effective than expected for system control performance. Implications for training design in process control are discussed.     

Effects of training on short- and long-term skill retention in a complex multiple-task environment

The paper reports the results of an experiment on the performance and retention of a complex task. This was a computer-based simulation of the essential elements of a spacecraft's life support system. It allowed the authors to take a range of measures, including primary and secondary task performance, system intervention and information sampling strategies, mental model structure, and subjective operator state. The study compared the effectiveness of two methods of training, based on low level (procedure-based) and high level (system-based) understanding. Twenty-five participants were trained extensively on the task, then given a 1-h testing session. A second testing session was carried out 8 months after the first (with no intervening practice) with 17 of the original participants. While training had little effect on control performance, there were considerable effects on system management strategies, as well as in structure of operator's mental model. In the second testing session, the anticipated general performance decrement did not occur, though for complex faults there was an increase in selectivity towards the primary control task. The relevance of the findings for training and skill retention in real work environments is discussed in the context of a model of compensatory control.     

A conceptual model of knowledge-based time-tabling system

This study aims to model a high school time-tabling task using the knowledge-based approach. The body of knowledge consists of a structural data set, rules sets and heuristics. A scheduling model is articulated to allocate teaching assignments to the time slot system by applying appropriate heuristics and rule sets. A scheduling engine is devised to allow the defining of assignments in any desired order using a heuristic function for enhancing the performance of the system; and to allow a search for the best slot on multiple feasible slots. The rule priorities may facilitate different time-tabling approaches.     

Modeling task completion time of in-vehicle information systems while driving with keystroke level modeling

The use of touchscreen-based in-vehicle information systems (IVIS) is increasing. To ensure safe driving, it is important to evaluate IVIS task performance during driving situations. Therefore, we proposed a model to assess the task completion time (TCT) of IVIS tasks while driving using a keystroke-level modeling (KLM) technique. The basic assumptions and heuristic rules of driver behaviors were considered. In addition, based on the characteristics of visual and manual IVIS interactions, we determined the basic unit operators (i.e., visual, manual, and mental operators). User experiments were conducted to determine the individual execution times of unit tasks and to measure the TCT of IVIS tasks while driving. Based on the heuristic rules for model development and individual task execution times, we derive a predictive model for the TCT of IVIS tasks. We used a regression analysis to validate the modeling procedure, showing that the observed TCT was found to have a strong positive correlation with the predicted time from the modeling process. The findings showed that the task completion time needed to perform a secondary task in a driving context can be predicted by KLM. This study provides meaningful insights into the design of touchscreen-based IVIS to enhance driving safety.     

Empirical Learning Using Rule Threshold Optimization for Detection of Events in Synthetic Images

We have developed an expert system for interpretation of passive sonar images. A key component of the system is a group of event detection rules whose conditions consist of tests against thresholds. Due to the complexity, variability and clumpiness (i.e., tendency towards highly nonuniform distribution) of the data, tuning these thresholds for good performance under all conditions is a difficult task. We have implemented a procedure for learning rule thresholds whereby the detection capability of each rule continually improves as more and more data is played through the system. The learning procedure contains the following components: 1) a windowing mechanism that adds exceptions (i.e., false alarms and missed detections) into a training database of positive and negative examples and 2) a genetic algorithm to optimize the thresholds with respect to the training database. The genetic training algorithm allows the developer to explicitly choose an operating point on the Receiver Operating Characteristic (ROC) curve of a rule. Experiments have verified 1) the superiority of this automated approach to selecting rule thresholds over manual techniques and 2) the improvement of rule performance with experience.     

An investigation of training strategies to improve alarm reactions

Researchers have suggested that operator training may improve operator reactions; however, researchers have not documented this for alarm reactions. The goal of this research was to train participants to react to alarms using sensor activity patterns. In Experiment 1, 80 undergraduates monitored a simulated security screen while completing a primary word search task. They received spatial, temporal, single sensor, or no training to respond to alarms of differing reliability levels. Analyses revealed more appropriate and quicker reactions when participants were trained and when the alarms were reliable. In Experiment 2, 56 participants practiced time estimation by simple repetition, performance feedback, or performance feedback and temporal subdivision. They then reacted to alarms based on elapsed time between sensor activity and alarm onset. Surprisingly, results indicated that participants did not benefit differentially from temporal interval training, focusing instead on advertised system reliability. Researchers should replicate these findings with realistic tasks and real-world complex task operators.