Rule-based expert systems

Expert systems. In situations where the statistical classifiers cannot be used, because of the complexity or inhomogeneity of the data, rule-based expert systems can sometimes be a solution. The complex images can be more readily described by rules than represented as simple feature vectors. Rules can be devised which cope with inhomogeneous data by, for example, triggering some specialised data-processing algorithms. 

The recognition ratios achieved by CBR systems developed as part of this project could not be bettered by either neural-network classifiers or rule-based expert system classifiers. In addition, CBR systems should be mote reliable than simple classifiers as they are programmed to recognise unknown data. The knowledge acquisition necessary to build CBR systems is less expensive than for expert systems, because it is simpler to describe the knowledge how to distinguish between certain types of data than to describe the whole data contents. 

Foulkes et al. (1988) have approached the synthesis of operating procedures from a more empirical angle. They have extended the work of Rivas and Rudd (1974) for the synthesis of complex pump and valve sequencing operations, relying on the use of logical propositions (implemented as rule-based expert systems), which capture the various types of constraints imposed on the states of a processing system. 

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. 

Ignizio, J.P, An Introduction to Expert Systems The Development and Implementation of Rule-Based Expert Systems, McGraw Hill, New York, 1991. 

OncoLogic Rule-based expert system for the prediction of carcinogenicity... 

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 ... 

Buchanan, B. G. Shortliffe, E. H. "Rule Based Expert-Systems" Addison-Wesley, 1984, Chap. 10. 

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. 

Hayes-Roth, F. Waterman, D.A. Lenat, D.B. Eds. Building Expert Systems, Addison-Wesley Publ. Co., Reading, MA, 1983. Buchanan, B.G. Shortliffe, E.H. Rule-Based Expert Systems Addison-Wesley Publ. Co., Reading, MA, 1984. 

C. Rojas-Guzman and M.A. Kramer. Comparison of belief networks and rule-based expert systems for fault diagnosis of chemical processes. Engineering Application of Artificial Intelligence, 6 191, 1993. 

Current generally applicable biodegradation models focus on the estimation of readily and nonreadily biodegradability in screening tests. This is because most experimental data are from such tests (e.g., MITI-I). There are far fewer data that are both quantitative and environmentally relevant (i.e., measured half-lives or rate constants). However, individual transformations and pathways are well documented in the literature. This allows for development of explicitly mechanistic models, making use of established group-contribution approaches, hierarchic rule-based expert systems, and probabilistic evaluation of possible transformation pathways. 

Expert systems have been defined as any formal systems , which make predictions about the toxicity of chemicals. All expert systems for the prediction of toxicity are built on experimental data and/or rules derived from such data (Dearden 2003). The expert systems can be further divided into two subclasses based on the method of generating rules. The one method is a knowledge- or rule-based expert system, for which experts/toxicologists create rules based on a list of structural features that have been related to a specified toxicity (Durham and Pearl 2001). An example of a typical knowledge- or rule-based system is DEREK, which will be described later. 

A detailed methodology for dryer selection, including the use of a rule-based expert system, has been described by Kemp [Drying Tech-nol. 13(5-7) 1563-1578 (1995) and 17(7 and 8) 1667-1680 (1999)]. A simpler step-by-step procedure is given here. 

Another aspect of reality that we often have to deal is with incomplete and uncertain knowledge. Most rule-based expert systems are capable of representing and reasoning with incomplete and uncertain knowledge. 

The generic rule-based expert system does not provide the ability to learn from experience. Unlike human experts, an expert system does not automatically modify its knowledge base, adapt it to a problem, or add new ones. 

Buchauau, B.G. aud Shortliffe, E.H., Eds., Rule-Based Expert Systems The MYCIN Experiments of the Stanford Heuristic Programming Project, Addisou-Wesley, Read-iug, MA, 1984. Out of priut electrouically available at http //www.aaaipress.org/Clas-sic/Buchauau/buchauau.html. 

ElAxpert is a rule-based expert system for enviromnental impact assessment, particularly designed for assessment of development projects at an early stage. EIAxpert is a generic and data-driven tool that can be adapted to different application domains. It was designed for the assessment of development projects for water resources in the Cambodian, Laotian, Thai, and Vietnamese parts of the Mekong Basin. The system relies on assessment rules derived from the Asian Development Banks Environmental Guideline Series [40]. 

EIAxpert is a rule-based expert system for environmental impact assessment, particularly designed for assessment of development projects at an early stage. 

The ANN is an artificial intelligent technique that has several distinct advantages over rule-based Expert System and Fuzzy Logic. The technique has been shown to be a feasible technique that estimates the material properties for the FGM design. The estimation accuracy is satisfactory. 

Recently, Diercksen and Hall (1) presented the OpenMol Program a proposal for an open, flexible and intelligent software system for performing quantum chemical computations. Central to their proposal was the observation that there is a close relationship between an abstract data type operation and a production rule in a rule-based expert system. The aim of this paper is to explore the establishment of a sound theoretical foundation for this relationship. 

Tools for rule-based expert systems (as well as manual methods) should evaluate the consistency and completeness of the rules. The TEIRESIAS program (Davis 1976) linked to the MYCIN infectious disease system was one of the first attempts to develop an automated verification tool. Later work by Suwa et al. (1982) for the ONCOCIN (clinical oncology) system examined a rule set as it was read into the system. This rule checker assumes that for each combination of attribute values appearing in the antecedent a corresponding rule exists. 

The LES system described by Nguyen et al. (1987) is a generic rule-based expert system building tool which has an extensive... 

Suwa, M. Scott, A.C. Shortliffe, E.M. An approach to verifying completeness and consistency in a rule-based expert system, AI Magazine 1982, 3(4), 16-21. 

Culbert, Chris, Riley, Gary and Savely, Robert T. (NASA) "Approaches to The Verification and Validation of Rule Based Expert Systems" NASA/JSC SOAR Conference, August 1987,... 

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