The knowledge engineer is the person with the expertise the expert system is trying to capture

Intelligent Techniques

Artificial intelligence and database technology provide a number of intelligent techniques that organizations use to capture individual and collective knowledge and to extend their knowledge base. Artificial intelligence (AI) is the effort to develop computer-based systems (both hardware and software) that behave as humans, with the ability to learn languages, accomplish physical tasks, use a perceptual apparatus, and emulate human expertise and decision making.

          Expert systems, case-based reasoning, and fuzzy logic are used for capturing tacit knowledge. An expert system is a knowledge-intensive computer program that captures the human expertise in limited domains of knowledge. Expert systems lack the generality and breadth of knowledge of humans because they only capture human expertise in very limited areas. Nevertheless, these systems aid decision making by asking relevant questions and explaining the reasons for adopting certain actions.

          The model of human intelligence that is used in expert systems is called the knowledge base. A series of rules (standard programming constructs such as IF-THEN) is one type of knowledge base. AI programs have far more rules than traditional programs and the rules tend to be interconnected and nested to a far larger degree. [Figure 12-11]

FIGURE 12-11 Rules in an AI program An expert system contains a number of rules to be followed when used. The rules are interconnected; the number of outcomes is known in advance and is limited; there are multiple paths to the same outcome; and the system can consider multiple rules at a single time. The rules illustrated are for simple credit-granting expert systems.

           The AI shell is the programming environment of an expert system. Although AI systems can be developed in just about any programming language, AI shells are user-friendly environments that can quickly generate user interface screens, capture the knowledge base, and manage the strategies for searching the rule base.

           The strategy to search through the rule base, called the inference engine, can be either forward or backward chaining. In forward chaining, the inference engine begins with the information entered by the user and searches the rule base to arrive at a conclusion. In backward chaining, an expert systems acts more like a problem solver. The strategy for searching the rule base starts with a hypothesis and proceeds by asking the user questions about selected facts until the hypothesis is either confirmed or disproved. [Figure 12-12]

FIGURE 12-12 Inference engines in expert systems An inference engine works by searching through the rules and �firing� those rules that are triggered by facts gathered and entered by the user. Basically, a collection of rules is similar to a series of nested IF statements in a traditional software program; however, the magnitude of the statements and degree of nesting are much greater in an expert system.

           Case-base reasoning involves storing past experiences of human experts represented by cases on a database. When a user encounters a new case, the system searches for similar parameters within the cases, finds the closest fit, and applies the solutions of the old case to the new case. Successful solutions are tagged to the new case and stored together in the knowledge base.

           Unsuccessful solutions are also appended to the new case along with an explanation of why the solutions didn't work. In contrast to rule-based expert systems, the knowledge base for the case-based reasoning system is continuously expanded and refined by users. [Figure 12-13]

FIGURE 12-13 How case-based reasoning works Case-based reasoning represents knowledge as a database of past cases and their solutions. The system uses a six-step process to generate solutions to new problems encountered by the user.

FIGURE 12-14 Implementing fuzzy logic rules in hardware The membership functions for the input called temperature are in the logic of the thermostat to control the room temperature. Membership functions help translate linguistic expressions such as warm into numbers that the computer can manipulate. Source: James M. Sibigtroth, �Implementing Fuzzy Expert Rules in Hardware,� AI Expert, April 1992. � 1992 Miller Freeman, Inc. Reprinted with permission.

FIGURE 12-15 How a neural network works A neural network uses rules it �learns� from patterns in data to construct a hidden layer of logic. The hidden layer then processes inputs, classifying them based on the experience of the model. Source: Herb Edelstein, �Technology How-To: Mining Data Warehouses,� InformationWeek, January 8, 1996. Copyright � 1996 CMP Media, Inc., 600 Community Drive, Manhasset, NY 12030. Reprinted with permission.

FIGURE 12-16 The components of a genetic algorithm This example illustrates an initial population of �chromosomes,� each representing a different solution. The genetic algorithm uses an iterative process to refine the initial solutions so that the better ones, those with the higher fitness, are more likely to emerge as the best solution. Source: From Intelligent Decision Support Methods by Vasant Dhar and Roger Stein, p. 65, � 1997. Reprinted by permission of Prentice Hall, Upper Saddle River, New Jersey.

           Intelligent agents automate routine tasks to help firms search for, filter, and act on information in electronic commerce, supply chain management, and other activities. Intelligent agents are software programs with a built-in or learned knowledge base that can carry out specific, repetitive, and predictable tasks for an individual user, business process, or software application. Intelligent agents are programmed to make decisions based on the user’s personal preferences, making them useful for filtering e-mail, scheduling appointments, or searching for the best price of airline tickets over the Internet. [Figure 12-17]

FIGURE 12-17 Intelligent agents in P&G�s supply chain network Intelligent agents are helping Procter & Gamble shorten the replenishment cycles for products such as a box of Tide.

What is knowledge engineer in expert system?

Is the person with the expertise or knowledge that the expert system is trying to capture?

What is the role of knowledge base in expert system?

What is expert system in knowledge management?

How are expert systems used in engineering?