Automation decision-making systems

In general, it appears that expert systems which combine symbolic/numeric processing capabilities are necessary to effectively automate decision-making in applications involving analytical and process instrumentation/sensors. Furthermore, these integrated decision structures will likely be embedded (67-69) within the analytical or process units to provide fully automated pattern recognition/correlation systems for future intelligent instrumentation. 

Automation in crystallographic structure solution decision-making systems... 

There are problems complicating this automation process, some are computer engineering, some practical, and some basically philosophical. These include the necessity for retaining optional ways of doing the calculations, the need for the user to retain control of the process, the restrictions placed on operation by the various computer systems, avoiding the waste of computer time, and the inherent difficulty usually encountered in automating decision making. 

A second use of feedback is to detect faults and failures in the controlled system, including the physical process and any computer controllers and displays. If the operator is expected to monitor a computer or automated decision making, then the computer must make decisions in a manner and at a rate that operators can follow. Otherwise they will not be able to detect faults and failures reliably in the system being supervised. In addition, the loss of confidence in the automation may lead the supervisor to disconnect it, perhaps under conditions where that could be hazardous, such as during critical points in the automatic landing of an airplane. When human supervisors can observe on the displays that proper corrections are being made by the automated system, they are less likely to intervene inappropriately, even in the presence of disturbances that cause large control actions. 

Automated decision making (ADM) The system selects the best option to implement and carries out that action, based upon a list of alternatives it generates (augmented by alternatives suggested by the human operator). This system, therefore, automates decision making in addition to the generation of options (as with decision support systems). 

In supply chain management (SCM), linkages play the roles of eon-ductor and sheet music. The SCOR model from the Supply-Chain Couneil is an example of one method for coordinating the supply chain. Another example, in the Toyota Production System, is the kanban system that signals the need for more parts. Proactive systems deseribed in Chapter 30 that use business rules to automate decision making are another example. The decision to use any particular technique at a point in time is an important supply chain design feature. 

DSS-based survivability architectures are not new. Schwaegerl et al. propose to use a DSS to increase the survivability of complex power systems [17]. Lee et al. developed a DSS system that improves the survivability of damaged submarines [11]. Relating to cyber survivability, a wide variety of existing software systems can be viewed as decision-support systems that employ automated decision making to improve the survivability of software systems [9,18,16]. 

Our cyber-survivability architecture is a decision-support system. Our approach diverges from existing approaches by using net-centric services to facilitate human and automated decision making. 

Figure 8. Multilevel expert systems outline. Automated experimental design and decision-making.

As noted in the introduction, a major aim of the current research is the development of "black-box" automated reactors that can produce particles with desired physicochemical properties on demand and without any user intervention. In operation, an ideal reactor would behave in the manner of Figure 12. The user would first specify the required particle properties. The reactor would then evaluate multiple reaction conditions until it eventually identified an appropriate set of reaction conditions that yield particles with the specified properties, and it would then continue to produce particles with exactly these properties until instructed to stop. There are three essential parts to any automated system—(1) physical machinery to perform the process at hand, (2) online detectors for monitoring the output of the process, and (3) decision-making software that repeatedly updates the process parameters until a product with the desired properties is obtained. The effectiveness of the automation procedure is critically dependent on the performance of these three subsystems, each of which must satisfy a number of key criteria the machinery should provide precise reproducible control of the physical process and should carry out the individual process steps as rapidly as possible to enable fast screening the online detectors should provide real-time low-noise information about the end product and the decision-making software should search for the optimal conditions in a way that is both parsimonious in terms of experimental measurements (in order to ensure a fast time-to-solution) and tolerant of noise in the experimental system. 

Eventually the workflow should be automated as much as possible into a computer-based decision support system (DSS) that aids the process of decision making when generating and using nontesting data for regulatory purposes. The DSS may prove invaluable as a means of promoting the regulatory use of in silico methods. 

The main rationale for a poststudy inspection is to confirm that the study was carried out to GXP and to the agreed plan, including all set criteria and specifications. The depth of this inspection can vary, ranging from confirmation of audit trail (sample integrity to final results) to evaluation of exceptions, that is, what decisions were made when repeat analyses were carried out, what triggered the repeat, were there appropriate SOPs, and were they followed. If there were exceptions not covered by SOPs, were decisions made objectively and consistently How much decision making was automated, for example, if repeat analyses were carried out, was a decision tree used, and if so, was this automated or manually applied If automated computerized systems are used, any manual intervention should arouse suspicion. In such a case, the level of auditing should be raised. 

Nonhuman participants in team meetings This refers to the use of unfacilitated DSS and expert systems that automate some aspects of the process of decision making. 

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