Rule-based system

Rule-based systems try to identify certain subsequences of amino acids that tend to have a particular secondary structure, such as sheets, a-helices, (I-strands, 

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Hybrid systems. Depending on the problem to be solved, use can also be made of a combination of techniques leading to a hybrid system. For example, a rule-based system may use neural networks for solving classification subproblems (as is described in [Hopgood, 1993]), or a combination of a rule-based and a CBR system can be used as in the system for URS data interpretation described later in this paper. 

Rule-based systems have the advantage that they usually report when they are incapable of recognising something. However, any disturbances in the inspection not foreseen during rule-hase construction will significantly lower the recognition ratio. 

Apart from the cost of knowledge acquisition, another disadvantage of rule-based systems is the difficulty of rule-base maintenance. Rule-base maintenance may be required when changes are made to the inspection system, the inspection procedures, or if differing constructions are inspected. The maintenance usually cannot be done by end-users. 

Tests were done on real data containing approx. 32(XX) nontrivial images. Of these approx. 25% were classified by the rule-based system and another 25% by the CBR system. The reliability was high - of 330 defects present in the data only two were classified as non-defects. We are currently working on further improving the recognition ratio and increasing the speed of the system. 

The end users of CBR systems should in principle be able to maintain the case-bases themselves and use the systems for varying inspection types (within certain limits). Adaptation of neural-network based systems, though possible by end-users, is difficult to be done reliably. Adaptation of rule-based systems usually has to be done by the rule-base designer. 

One variation of rule-based systems are fuzzy logic systems. These programs use statistical decision-making processes in which they can account for the fact that a specific piece of data has a certain chance of indicating a particular result. All these probabilities are combined in order predict a final answer. 

Rules. Rules, first pioneered by early appHcations 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 IE—THEN rules. IE—THEN rules are essentially association pairs, specifying that IE 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... 

Rules may represent either guidelines based on experience, or compact descriptions of events, processes, and behaviors with the details and assumptions omitted. In either case, there is a degree of uncertainty associated with the appHcation of the rule to a given situation. Rule-based systems allow for expHcit ways of representing and dealing with uncertainty. This includes the representation of the uncertainty of individual rules, as weU as the computation of the uncertainty of a final conclusion based on the uncertainty of individual rules, and uncertainty in the data. There are numerous approaches to uncertainty within the rule-based paradigm (2,35,36). One of these approaches is based on what are called certainty factors. In this approach, a certainty factor (CF) can be associated with variable—value pairs, and with individual rules. The certainty of conclusions is then computed based on the CF of the preconditions and the CF for the rule. For example, consider the foUowing example. 

Tetko, L, Livingstone, D. J. Rule-based systems to predid lipophilidty. 

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 ... 

Rules seemingly have the same format as IF.. THEN.. statements in any other conventional computer language. The major difference is that the latter statements are constructed to be executed sequentially and always in the same order, whereas expert system rules are meant as little independent pieces of knowledge. It is the task of the inference engine to recognize the applicable rules. This may be different in different situations. There is no preset order in which the rules must be executed. Clarity of the rule base is an essential characteristic because it must be possible to control and follow the system on reasoning errors. The structuring of rules into rule sets favours comprehensibility and allows a more efficient consultation of the system. Because of the natural resemblance to real expertise, rule-based expert systems are the most popular. Many of the earlier developed systems are pure rule-based systems. 

Widely used in rule-based systems, input-output matching approaches are used in conventional expert systems going back to the earliest systems. 

The information to which the rule is applied might be extracted from the knowledge base, it might be provided by the user in response to questions from the ES, or it may be provided by combining the two. An expert system that uses rule-based reasoning is, quite reasonably, known as a rule-based system. This is the most widely used form of expert system in science, and it is on this type of system that this chapter concentrates. 

Classical process synthesis consists of the synthesis of the alternatives, their analysis and final evaluation. Hurme and Jarvelainen (1995) have presented a combined process synthesis and simulation system consisting of an interactive rule-based system which is used for generating process alternatives (Fig. 10). The process alternatives are simulated, costed and evaluated through profitability analysis. The developed system concept combines process synthesis, simulation and costing with uncertainty estimation. 

Adding information from rule-based system c ... 

This rule-based system allows expressing the membership criteria for each protein family in a formal language. Furthermore, subfamilies have been introduced to meet the SWISS-PROT standard more closely. For example, the ribosomal protein LI family contains eukaryotes as well as prokaryotes. But the annotation added to TrEMBL entries of this family obviously depends on the taxonomic kingdom. The description reads "50S RIBOSOMAL PROTEIN Ll" for prokaryotes, archaebacteria, chloroplasts, and cyanelles, and "60S ribosomal protein lioa" for nuclear encoded proteins of eukaryotes. 

J.M. Nigro and M. Rombaut. IDRES A rule-based system for driving situation recognition with uncertainty management. Information Fusion, 4(4) 309-317, 2003. 

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. 

The fact that several representations are possible automatically necessitates the development of rules which would allow a researcher to decide upon a preferred representation. To this end, lUPAC has developed an elaborate rules-based system using the seniority of subunits , the direction of citation, etc. [65]. However, rules-based systems are subject to the same limitations as nomenclature systems in that they, too, suffer from (potential) historical discontinuities and require acceptance by a broad community. 

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