IF-THEN-rules

If the pattern does not fit into an immediately identifiable pattern, the process worker may then consciously apply more explicit "if-then" rules to link the various symptoms with likely causes. Three alternative outcomes are possible from this process. If the diagnosis and the required actions are very closely linked (because this situation arises frequently) then a branch to the Execute Actions box will occur. If the required action is less obvious, then the branch to the Select/Formulate Actions box will be likely, where specific action rules of the form "if situation is X then do Y" will be applied. A third possibility is that the operating team are unable or imwilling to respond immediately to the situation because they are uncertain about its implications for safety and/or production. They will then move to the Implications of plant state box. 

The contents of a knowledge base, the facts and rules, or heuristics about a problem will be discussed shortly. The problem-solving and inference engine is the component of the system that allows rules and logic to be applied to facts in the knowledge base. For example, in rule-based expert systems, "IF-THEN" rules (production rules) in a knowledge base may be analyzed in two ways ... 

The logical deduction portion of the program is based on IF-THEN rules. FACTS, acquired both as the result of logical deductions and by querying the user, are stored in similar data structures. Because the branch points in the problem are also logical deductions, they are stored in a data structure similar to the FACTS. The branch points contain additional flow of control information that relates to the hierarchy of the problem. The difference between FACTS and branch points is transparent to the logical deduction portion of the program. 

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. 

Expert systems are computer programs that simulate the decision-making process of human experts. The hallmark of expert systems is that decisions are based on heuristics (rules of thumb) when data is incomplete or there is not enough time to consider all possibilities [19]. An expert system can be a set of IF-THEN rules in FORTRAN, or it can be a written in one of the languages designed for expert systems, such as LISP. Expert systems can be used off-line to aid in cure cycle selection, or they can be used as real-time advisers or controllers. 

The development of expert systems need not be costly. There are several expert system shells commercially available, and an expert in artificial intelligence (AI) is no longer needed to program them. Simple IF-THEN rules can easily be programmed in more commonly used languages like FORTRAN and BASIC. In fact, the McDonnell Aircraft expert system referred to earilier was programmed in FORTRAN. Cost depends on the number of rules and... 

Rules. Rules, first pioneered by early applications such as Mycin and Rl, are probably the most common form of representation used in knowledge-based systems. The basic idea of rule-based representation is simple. Pieces of knowledge are represented as IF—THEN rules. IF—THEN rules are essentially association pairs, specifying that IF certain preconditions are met, THEN certain fact(s) can be concluded. The preconditions are referred to as the left-hand side (LHS) of the rule, while the conclusions are referred to as the right-hand side (RHS). In simple rule-based systems, both the... 

In fuzzy logic, the use of fuzzy if-then rules is governed by the calculus of fuzzy rules, CFR. A major part of CFR is the Fuzzy Dependency and Command Language, or FDCL for short. Basically, FDCL is a fuzzy programming language that provides a powerful tool for the representation and manipulation of imprecise or ill-defined dependencies. Two issues play pivotal roles in FDCL. The first is interpolation, and the second relates to the induction of rules from observations. 

The problem of interpolation may be described as follows. Assume that we have a collection of fuzzy if-then rules that express the dependency of the linguistic variable Y on the linguistic variables X, ...,X , with the ith rule having the form if Aj is A j and is A, , then Y is... 

Interpolation serves an important purpose it greatly reduces the number of rules that are needed to describe a dependency. Thus, in many of the applications of fuzzy logic in control, the number of rules is of the order of 20 and rarely exceeds 90. By contrast, when crisp if-then rules are used, their number may be in the hundreds. 

In many of the early applications of fuzzy logic, the s and B s in the if-then rules had to be calibrated by cut-and-trial to achieve a desired level of performance. During the past few years, however, the techniques related to the induction of rules from observations have been developed to a point where the calibration of rules—by induction from input-output pairs—can be automated in a wide variety of cases. Particularly effective in this regard are techniques centered on the use of neural network methods and genetic computing for purposes of system identification and optimization. Many of the so-called neuro-fuzzy and fuzzy-genetic systems are of this type. 

In CW, the initial data set is assumed to consist of a collection of propositions expressed in a natural language or, more particularly, in the form of a collection of fuzzy if-then rules. The result of computation, that is, the terminal data set, is likewise a collection of propositions expressed in a natural language. 

A simple example of CW is the following. Assume that a function, /, Y=/(X), is described in words by the fuzzy if-then rules ... 

With the help of experts, the proliferation critical path is being formulated as logically connected if-then rules. The primary purpose of this formulation is to recognise and place information (e.g., export data) in the appropriate place(s) in the critical path structure. The critical path takes into account the possibility of shortening any of the paths to weaponisation at each of the fuel-cycle steps through external procurement (e.g., procurement of source material, UF, enriched uranium, etc.) and technical assistance. Nuclear activity profile for a given country is then obtained from a model presented in Fig. 11.8. 

Instead of representing knowledge in a static way, rule-based systems represent knowledge in terms of rules that lead to conclusions. A simple rule-based system consists of a set of if-then rules, a collection of facts, and an interpreter controlling the application of the rules by the given facts. Other important knowledge representation techniques are frames and semantic networks [1],... 

A simple if-then rule in a login procedure can look like the following ... 

If-Then Rules are collected in a rule base and describe problem situations and the actions an expert would perform in those situations. 

Pudenz S, Briiggemann R, Bartel HG (2002) QSAR of Ecotoxicological Data on the Basis of Data-Driven If-Then-Rules. Ecotoxicology 11 337-342... 

Forward-chaining A technique in logical inference using IF-THEN rules. All possible conclusions are drawn from a list of known facts. Because each new conclusion (THEN-clause) can be used as a fact (IF-clause) to derive other conclusions, the program iterates until no new facts can be concluded. 

Production rule Another name for IF-THEN rules used in representing information in an expert system s knowledge base. 

Data of different units and uncertainties can be combined using IF... THEN... rules, based on expert knowledge. Recently, the application of the traceability concept on ecotoxicological studies has been described (Ahlf and Heise, 2007). A suggestion for an ecotoxicological classification system for sediments based on fuzzy sets and fuzzy expert systems is under development (see Chapter 6.2). 

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