人工智能与专家系统外文文献译文和原文.docx

人工智能与专家系统外文文献译文和原文人工智能与专家系统外文文献译文和原文 ARTIFICIAL INTELLIGENCE AND EXPERT SYSTEM 1. History of AI The seed of AI were sown only two years after General Electric installed the first computer for business use. The year was 1956, and the term artificial intelligence (AI) was coined by john McCarthy as the theme of a conference held at Dartmouth College . That same year, the first AI computer program, called Logic Theorist was announced. Logic Theorists limited ability to the reason (proving calculus theorems) encourage researchers to develop another program called the General Problem Solver (GPS), which was intended to solve problems of all kinds. The task turned out to be more then the early pioneers could handle. AI research continued, but it took backseat to the less ambitious computer applications such as MIS and DSS. Over time, however, persistent research continued to push back the frontiers of using the computer for tasks that normally require human intelligence. 2. Areas of AI AI is currently being applied in business in the form of knowledge systems, which use human knowledge to solve problems. The most popular type of knowledge-based system is the expert system. An expert system is a computer program that attempts to represent the knowledge of human expert in the form of heuristics is derived from the same Greek root as the word eureka, which means “to discover”. A heuristic is, therefore, a rule of good guessing. Heuristics do not guarantee results as absolutely as do conventional algorithms that are incorporated into DSSs, but they offer results that are specific enough most of the time to be useful. The heuristics allow the expert system to function in a manner consistent with a human expert, advising the user on how to solve a problem. Since the expert system functions as a consultant, the act of using it is called a consultation-the user consults the expert system for advice. In addition to expert system, AI includes work in the following areas: neural networks, perceptive systems, learning, robotics, AI hardware, and natural language processing. These areas are illustrated the way that one area can benefit the others. 3. The Appeal of Expert System The concept of expert system is based on the assumption that an experts knowledge can be captured in computer storage and then applied by others when the need arises. An expert system offers unique capabilities as a decisions support system. First, an expert system offer the opportunity to make decisions that exceed the managers capabilities . For example, a new investment officer for a bank can use an expert system designed by a leading financial expert and, in doing so, incorporate the experts knowledge into his or reaching a particular solution. Very often, the explanation of how a solution was reached is more valuable than the solution itself. 4. An Expert System Model The model of an expert system consists of four main parts. The knowledge base houses the accumulated knowledge of the particular problem to be solved. The inference engine provides the reasoning ability that interprets the contents of the knowledge base. The expert and the knowledge engineer use the development engine to create the expert system. 1. The User interface The user interface enables the manager to enter instructions and information into the expert system and to receive information from it. The instructions specify the parameters that guide the expert system through its reasoning processing. The information is in the form of values assigned to certain variables. (1) Expert System Inputs The most popular interface format today is the graphical user interface, which features a Windows look. Some systems employ a custom interface tailored to the problem being solved. For example, the screen might display a drawing of a mechical assembly. (2) Expert System outputs Expert system are designed to recommend solutions. These solutions are supplemented by explanations. There are two types of explanation: Expert system are designed to recommend solutions. These solutions are supplemented by explanations while the expert system performs its reasoning. Perhaps the expert system will prompt the manager to enter some information. The manager asks why the information is needed. The expert system provides an explanation. Explanation of the problem solution. After the expert system provides a problem solution, the manager can ask for an explanation of how it was reached. The expert system will display each of the reasoning steps leading to the solution. Although the inner working of the expert system can be complex , the user interface is user-friendly. A manager accustomed to interacting with a computer should have no difficulty in using an expert system. 2. The Knowledge base The knowledge base contains both facts that describe the problem area and knowledge representation techniques that describe how the facts fit together in a logical manager. The term problem domain is used to describe the problem area. （1）Rules A popular knowledge representation technique is the use of rules specifies what to do in a given situation technique is the use of rules. A rule specifies what to do in a given situation and consists of two parts: a condition that may or may not be true and an action to be taken when the condition is true. An example of a rule is: IF ECONOMIC.INDEX>1.20 AND SEASONAL.INDEX>1.30 THEN SALES.OUTLOOK=”EXCELLENT” All of the rules contained in an expert system are called the rule set. The rule set can vary from a dozen of rules .A dozen of rules for a simple expert system,and 500, 1,000, or 10,000 rules for a complex one. (2) Network of Rules The rules of a role set are not physically linked, but their logical relationships can be illustrated with a hierarchical diagram. The rules at the bottom of the hierarchy provide evidence for the rules on the upper levels. The evidence enables the rules on the upper levels to produce conclusions. The top level might consist of a single conclusion, indicating that the problem has only a single solution. The term goal variable is used to describe the solution, which can be a computed value, an action to be taken, or some other recommendation. For example, if an expert system is to advise top-level management on whether to enter a new market area, a value of Yes or Not would be assigned to the single-goal variable MARKET DECISION. It is also possible for the top level of the hierarchy to include multiple conclusions, indicting the possibility of more than one solution. An example is an expert system that makes recommendations concerning the best strategy to follow in reacting to increased competitive activity. The system might select from among possible strategies of improving the quality of the firms products, investing more in advertising, or lowering prices. 3. The Inference Engine The inference engine is the portion of the expert system that performs reasoning by using the contents of the knowledge base in a particular sequence. During the consultation, the inference engine examines the rules of the knowledge base one at a time, and when a rules condition is true, the specified action is taken. In expert systems terminology, the rule is “fired” when the action is taken. Two main methods have been devised for the inference engine to use in examining the rules: forward reasoning and reverse reasoning. (1) Forward reasoning In forward reasoning, also called forward chaining, the rules are examined one after another in a certain order. The order might be the sequence in which the rules were entered in to the rule set, or it might be some other sequence specified by the user. As each rule is examined, the expert system attempts to evaluate whether the conditions true or false. RULE EVALUSTION. When the condition is true, the rule is fired and the next rule is examined. When the condition is false, the rule is not fired the next rule is examined. It is possible that a rule cannot be evaluated as true or false. Perhaps the condition includes one or more variables with unknown values. In that case, the rule condition is unknown. When a role condition is unknown, the rule is not fired and the next rule is examined. THE ITERAIIVE REASONING PROCESS. The process of examining one rule after the other continues until a complete pass has been made through the entire rule set. More than one pass usually is necessary to assign a value to the goal variable. Perhaps the information needed to evaluate one rule is produced by another rule that is examined subsequently. For example, after the eleventh rule is fired, the fifth rule can be evaluated on the next pass.The passes continue as long as it is possible to fire rules. When no more rules can be fired, the reasoning process ceases. (2) Reverse Reasoning In reverse reasoning, also called backward chaining, the inference engine selects a rule and regards it as a problem to be solved. Using the rule set as shown in figure 20-1. Rule 12 is the problem, since it assigns a value to the goal variable P. The inference engine attempts to evaluate Rule 12 but recognizes that Rule 10 or Rule 11 must be evaluated first. Rule 10 and 11 become sub problems of Rule 12. The inference engine then selects one of the subproblems to evaluate, and the selected subproblem becomes the new problem. Figure20-1 Rules set THE FIRST LOGCAL PATH IS PURSUED. We will assume that Rule 10 becomes the problem. The inference engine then determines that Rule 7 and 8 must be evaluated before Rule 10 can be evaluated. Rules 7and 8 become the subproblems in this manner, searching for a rule that can be evaluated. THE NEXT LOGICAL PATH IS PURSUED. When the expert system attempts to evaluate Rule 11, Rule 9 becomes the problem; it can be evaluated using the outcomes of Rules 4 and 5. Because both Rules 4 and 5 are true, Rule 9 can be evaluated as true without the need to examined Rule 6. Once Rule 9 is fired, Rule11 can be fired as well. This makes it possible to assign a value to goal variable P, since Rule 12 is fired if either Rule 10 or 11 is true. (3) Comparing Forward and Reverse Reasoning Reverse reasoning proceeds faster than forward reasoning, because it does not have to consider all of the rules and does not make multiple passes through the rule set. Reverse reasoning is especially appropriate when: l There are multiple goal variables. l There are many rules. l All or most all of the rule do not have to be examined in the process of reaching a solution. Some inference engines are designed to perform both forward and reverse reasoning.The user can specify which one to use. 4. The Development Engine The forth major component of the expert system is the development engine, which is used to create the expert system. When the inference engine consists of rules, this process involves building the rule set. There are two basic approaches: programming languages and expert system shells. (1) Programming Language You can create an expert system using any programming language; however, two are especially well suited to the symbolic representation of the knowledge base: Lisp and Prolog . Lisp was developed in 1959 by john McCarthy ( one of the members of that first AI meeting ) , and Prolog was begun by Alain Colmerauer at the University of Marseilles in 1972. (2) Expert System Shells One of the first expert systems was Mycin, developed by Edward Shotlffle and Stanley Cohen of Stanford University, with the help of Stanton Axline, a physician. Mycin was created to diagnose certain infectious diseases. When the success of Mycin had been established, the developers looked for other ways tailored to apply their accomplishments. They discovered that the Mycin inference engine could be tailored to another type of problem by replacing the Mycin knowledge base with one reflecting the other problem domain. This finding signaled the start of a new approach to building expert system: the expert system sell. An expert system sell is a ready-made processor that can be tailored to a specific problem domain through the addition of the appropriate knowledge base. Today, most of the interest in applying expert system to business problems involves the use of sells. An example of a problem domain that lends itself to an expert system shell is help desk support. A help desk is a unit with-in the organization that provides technical help to users as well as to their own information specialists. In its most basic form, the help desk consists of one or more technical experts who receive users telephone calls for help. The user explains the problem and the technical expert suggests ways to solve it, perhaps referring to product manuals or other written sources. The help desk problem is so pervasive that a Helpdesk Institute was formed to facilitate dialogue among firms and industries with help desk expert system shells. When a firm uses one of the shells, it must populate the knowledge base with data concerning its own hardware and applications software. A software vendor can populate its knowledge base with data describing its software products, and so on. When a help desk expert system is used, either the user or the help desk staff member communicates directly with the system, and the system attempts to resolve the problem. One test of the degree of sophistication of artificial intelligence is whether the user cannot determine if he or she is interfacing with a human or a computer. This test has been called the Turing Test, in honor of the great pioneers in computer science, Alan Turing. The help desk expert systems use a variety of knowledge representation techniques. A popular approach is called case-based reasoning (CBR), which uses historical data as the basis for identifying problems and recommending solutions. Some systems employ knowledge expressed in the form of a decision tree, a network-like structure that enables the user to progress from the root through the network of branches by answering questions relating to the problem. The path leads the user to a solution at the end of branch. Expert system shells have brought artificial intelligence within the reach of firms that do not have the resources necessary to develop their own systems using programming language. In the business area, expert system shells are the most popular way for firms to implement knowledge-base system. 5. Advantages and Disadvantages of Expert Systems As with all computer applications, expert systems offer some real advantages; but there are also disadvantages. The advantages can accrue to both managers and the firm. 1. The Advantages of Expert Systems to Managers l Managers use expert systems with the intention of improving their decision-making. The improvement comes from being able to: l Consider More Alternative. An expert system