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5.1. CASE-BASED REASONING (CBR)CBR relies on expertise in the form of worked cases. CBR works by measuring how similar a new situation is to an existing case in the case base, and retrieving the most similar (most relevant) case. A case consists of the attributes and values of a problem situation, as well as its solution. CBR does not require much of a domain theory -- it requires nothing more than a representative sample of stereotypical worked cases. Knowledge discovery and database mining techniques can often generalize, induce, and transform cases, or portions of cases, into useful rules. Case-based reasoning should be used when:
5.2. RULE-BASED SYSTEMS (RBS)RBS use many small slivers of knowledge organized into conditional If-Then rules. Inference engines for RBS are either goal-driven, backward-chaining, or data-driven, forward-chaining, depending on the type of application or generic task. Rules often represent heuristics --shortcuts or rules-of-thumb for solving problems. Regarding the amount of structure, RBS fall in between CBR and MBR -- rules of thumb are abstracted and generalized from experience into small chunks of knowledge. Both CBR and RBS can be developed incrementally and can provide some value in an unfinished state. RBS can also be transformed into objects or frames using knowledge discovery and database mining techniques. Rule-based systems should be used when:
5.3. MODEL-BASED REASONING (MBR)MBR provides a representational and conceptual framework for knowledge that defines both knowledge structures and inferencing methods. MBR defines and structures relevant domain objects/concepts, their attributes, and their behaviors in order to organize work in complex domains and perform simulations. MBR also defines the relationships between the objects in terms of class hierarchies, composition, and causation. The most basic knowledge structure, governing all types of knowledge, is the <Object Attribute Value> triple. MBR encompasses, represents, and organizes all types of knowledge, including CBR and RBS, as well as databases, text, images, and other media. Types of MBR are object-based, frame-based, and domain-specific; MBR models can also be categorized as quantitative or qualitative. MBR requires a well-structured, well-understood domain theory. In simulation, MBR components are often so tightly linked together that MBR has limited value without a completed model. MBR is also very useful for organizing and structuring complex domains and work processes. Model-based reasoning should be used when:
6. APPROACHES TO IDENTIFYING EXPERT SYSTEM APPLICATIONSFollowing is a list of approaches in which potential ES applications have been found:
6.1. KNOWLEDGE-INTENSIVE ORGANIZATIONAL FUNCTIONS APPROACHOne key to finding promising ES applications is to locate industries, organizational functions, business activities, and work system components where work is information- and knowledge-intensive. The following organizational functions are information- and knowledge-intensive:
6.2. KNOWLEDGE-INTENSIVE WORK ACTIVITIES APPROACHOn a finer-grained level within an organization, activities within functions can also be classified as information- and knowledge-intensive. These following business activities that are knowledge-intensive can occur within any of the organizational functions:
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