KnowledgeBase ( Expert Systems)

Expert systems are computer programs designed to simulate the cognitive problem-solving behavior of human experts in a specified, well-defined area. These programs contain stores of knowledge, usually in the form of facts and rules, together with procedures for processing this knowledge to infer solutions to problems normally requiring the attention of a human expert. Expert system development: Framework for development, All frameworks demonstrate an evolutionary progression from a small prototype to an operational system, similar to the process. According to Harmon and King, there are two approaches to developing expert systems. They choose approaches depending on whether a small or a large expert system. In Small Expert System. Harmon and King somewhat arbitrarily refer to the small expert system as a "knowledge system." They elect to use this distinction because the smaller systems do not necessarily capture the knowledge or human experts. According to Harmon and King, knowledge system knowledge bases may contain other than human "expert' knowledge. However, these authors do not imply that then small knowledge system is to be slighted. Small Knowledge System development steps Step 1 Select a tool and implicitly commit yourself to a particular consultation paradigm. Step 2 Identify a problem and then analyze the knowledge to be included in the system. Step 3 Design the system. Initially this involves describing the system on paper. It typically involves making flow diagrams and matrices and drafting a few rules. Step 4 Develop a prototype of the system using the tool. This involves actually creating the knowledge base and testing it by running a number of consultations. Step 5 Expand, test, and revise the system until it does what you want it to do. Step 6 Maintain and update the system as needed. The role of the expert system developer has been largely replaced in the small system by the expert system development tool and the knowledge of the ultimate system user. Harmon and King feel small systems will improve decision making and enhance productivity in many ways Large Expert System Harmon and King envision the development of large expert systems as a team effort with special training in knowledge engineering. The expert system development consists of the following phases. Phase I: Problem selection. Phase I is the foundation upon which the development of the expert system rests. Phase I consists of identifying the problem domain and specific task; identifying a cooperative expert; determining a tentative approach to solving the problem; performing a cost and benefit analysis of potential alternatives; and, constructing a plan to guide the development effort. Phase II: Prototype development. Phase II is the development of a small-scale version or prototype of the desired final expert system. It serves as a validation of important concepts and relationships and allows the knowledge engineer to become familiar with the problem domain. During Phase II, a development tool is selected, and a detailed expert system design is completed. Phase III: Complete expert system development. Phase III is further development and refinement of the prototype expert system. It consists of enlarging the knowledge base, improving the user interface, reassessing the content of the knowledge base, and monitoring and controlling the expert system's performance. Phase IV: Expert system evaluation. Phase IV is an evaluation of the expert system against specific criteria identified during the prototyping phase (Phase II) Phase V: Expert system integration. Phase V involves integrating the expert system into the using environment. This encompasses all the efforts necessary to tie the expert system into the present workings of the particular business. This would include integrating the expert system with existing data bases, other available software, and any existing hardware. Phase VI: Expert system maintenance. This phase allows for any necessary upgrading of the system to meet evolving needs of the using organization. It may include expert system modification or updating of the expert system's knowledge base.