Action expert system

Expert systems are widely applied in design and planning where they can give instructions on how to design systems for given criteria or constraints. Expert systems are also used for simulation tasks Starting from an initial state, these systems deduce subsequent states with the aim of simulating possible outcomes for certain actions. 

The earliest practical use of an expert system was made in the software named MYCIN for diagnosing a toxic poison from the symptoms of a patient and recommending the antidote (62). This type of activity is generally carried out by a human expert who processes information about a situation (in this case, symptoms of a patient), refers to the expert s experience and expert knowledge, and then recommends action (in this case, the antidote). 

In the first stages of the development of an Action plan all control options are considered. In the case of lakes, this process is aided by a PC-based expert system , PACGAP, which looks at the physical and chemical characteristics of the lake to determine the most likely option for control. Once further, more detailed information has been collected on the lake s nutrient inputs and other controlling factors, amore complex interactive model can be used (Phytoplankton Response To Environmental CHange, PROTECH-2) to define the efficacy of proposed control options more accurately. This model is able to predict the development of phytoplankton species populations under different nutrient and stratification regimes. 

The most popular representation scheme in expert systems is the mle-based scheme. In a rule-based system the knowledge consists of a number of variables, also called attributes, to which a number of possible values are assigned. The rules are the functions that relate the different attributes with each other. A mle base consists of a number of If... Then... mles. The IF part contains the conditions that must be satisfied for the actions or conclusions in the THEN part to be valid. As an example, suppose we want to express in a mle that a compound is unstable in an alkaline solution if it contains an ester function. In a semi-formal way the mle can be written ... 

An expert system does much more than extract information from a database, format it, and offer it up to the user it analyzes and processes the information to make deductions and generate recommendations. Because an ES may be required to present alternative strategies and give an estimate of the potential value of different courses of action, it must contain a reasoning capacity, which relies on some sort of general problem-solving method. 

The decision-making engine in the CS is the set of classifier condition-action rules therefore, the key to a successful application is a well-constructed set of rules. If the control problem is straightforward, the necessary classifiers could, in principle, be created by hand, but there is rarely much point in doing this. A single classifier is equivalent to a production rule, the same structures that form the basis of most expert systems if a set of classifiers that could adequately control the environment could be created by hand, it would probably be as easy to create an equivalent expert system (ES). As an ES is able to explain its actions but a CS is not, in these circumstances, an ES would be preferable. 

Communication Satellites. The next example illustrates an expert system similar to those under development in process control and instrumentation companies. These systems are designed to diagnose faults and suggest corrective actions. 

The decision for each example is expressed as an "action-next state" pair. The "action" is a reference to executable Radial code, which consists of a sequence of Radial statements. These statements may contain references to external programs in various languages (this will be discussed further later). The "next state" describes the context to which control is to pass after the action is completed. For diagnostic expert systems, such as TOGA, the next state will usually be the "goal" state of the module. This passes control back to the calling module. For procedural expert systems, such as robotics and instrumentation control applications, the control will be transferred between several states within a module to Implement looping. 

This paper reflects the past activities of some of its authors in computer modeling of the chemical aspects of biological systems. This activity requires expertise in both model-building and in the relevant biology. It also involves examination of the actions of and results obtained by experts, like that routinely done in building expert systems. It also involves keeping track of and coherently explaining sequences of decisions, which expert systems are equipped to do. 

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. 

Evolving from efforts [22] to use the best features of trial-and-error, process model, expert system, and expert model approaches, QPA [23-25] combines KBES traits with online dielectric, pressure, and temperature data to implement autoclave curing control. QPA combines extensive sensor data with KBES rules to determine control actions. These rules determine curing progress based upon process feedback, and implement control action. QPA adjusts production parameters on-line as such—within the limits of its heuristics—QPA can accommodate batch-to-batch prepreg variations. 

In any expert system, explanations of the decisions made are important, both for instruction of the user and for maintenance of the system. Explanations in GSH take several forms. There are explanations for the development steps and their ordering provided by the designer of the knowledge base. Detailed explanations of the rules activated, formulae used, or individual scores of actions can be generated if required, and canned text and literature references are provided for general knowledge. 

Let us think abont how we can make a first hypothetical approach to what an expert system wonld constitnte. A standard model in the science of knowledge management is the knowledge pyramid, which basically describes the quantitative and logical relationship among data, information, knowledge, and action (please also refer to Fignre 2.1). 

Rule-based programming is one of the most commonly used techniques for developing expert systems. In this programming paradigm, rules are used to represent heuristics, which specify a set of actions to be performed for a given situation. A rule is composed of an if-portion and a then-portion. The if-portion of a rule is a series of patterns that specify the facts, or data, that cause the rule to be applicable. The process of matching facts to patterns is called pattern matching. 

Action is one of the possible results of the reasoning process in an expert system. In contrast to assignments, actions are triggered processes that lead to a change in the software environment. 

The authors have been involved in the development and deployment of advanced oil analysis systems including completely automated expert systems for data interpretation and maintenance action recommendation. The data analysis principles utilized in these systems are not new. The Canadian Pacific Railway first utilized expert systems for oil data interpretation in 1985 [30]. Subsequently, the paradigm utilized in... 

There were no caveats or warnings in either case that the users were dealing with test data, or "naked numbers," and betatest software. Some of the users of the beta versions of the expert systems turned out to be companies regulated by EPA. Third, the potential existed for any one of the users to capture the data and use it in a permit application proceeding or as part of the defense against an enforcement action. In the private sector, these risks would be equal to sending key chapters of your company s business plan to the competition. 

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