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1、 人工智能與專家系統(tǒng)外文文獻(xiàn)譯文和原文 人工智能與專家系統(tǒng)外文文獻(xiàn)譯文和原文 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 j

2、ohn 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 Theorist’s limited ability to the reason (proving calculus theorems) encourage researchers to develop another program called the General Probl

3、em 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

4、 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 e

5、xpert 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

6、 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 sy

7、stem 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.Th

8、ese 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 expert’s knowledge can be captured in computer storage and then applied by others when the need arises.An expert system offers unique c

9、apabilities as a decisions support system.First, an expert system offer the opportunity to make decisions that exceed the manager’s 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 expert’

10、s 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

11、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

12、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 popula

13、r 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 recomm

14、end 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 en

15、ter 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 t

16、he 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

17、 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

18、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

19、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

20、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 conclus

21、ion, 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

22、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 t

23、he best strategy to follow in reacting to increased competitive activity.The system might select from among possible strategies of improving the quality of the firm’s products, investing more in advertising, or lowering prices. 3.The Inference Engine The inference engine is the portion of the exper

24、t 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 rule’s condition is true, the specified action is taken.In expert systems terminology

25、, 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 ce

26、rtain 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, t

27、he 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 unk

28、nown.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 v

29、alue 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

30、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

31、 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 proble

32、m. 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 eva

33、luated.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 fir

34、ed, 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 r

35、ules 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

36、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

37、 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 wa

38、s 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 Sta

39、nford 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

40、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 spec

41、ific 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-

42、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 t

43、o 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 knowledg

44、e 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,

45、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

46、, 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

47、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

48、 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

49、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 f

50、rom being able to: l Consider More Alternative.An expert system can enable a manager to consider more alternatives in the process of solving a problem.For example, a financial manager who has been able to track the performance of only thirty stocks because of the volume of data that must be consider

51、ed can, with the help of an expert system, track 300.By being able to consider a greater number of possible investment opportunities, the likelihood of selecting the best ones is increased.l Apply a Higher Level of Logic.A manager using an expert system can apply the same logic as that of a leading

52、expert in field.l Devote More Time to Evaluating Decision Results.The manager can obtain advice from the expert system quickly, leaving more time to weigh the possible results before action has to be taken.l Make More Consistent Decisions.The computer does not have good days and bad days as the huma

53、n manager does, Once the reasoning is programmed into the computer, the manager knows that the same solution process will be followed for each problem.2.The Advantages of Expert Systems to the Firm l A firm that implements an expert system can expert: l Better Performance for the Firm.As the firm’s

54、 managers extend their problem solving abilities through the use of expert system, the form’s control mechanism is improved.The firm’s better able to meet its objectives.l To maintain Control over the Firm’s Knowledge.Expert systems afford the opportunity to make the experienced employees’ knowledge

55、 more available to newer, less experienced employees and to keep that knowledge in the firm longer—even after the employees have left.3.The Disadvantages of Expert systems Two characteristics of expert systems limit their potential as a business problem-solving tool.First, they cannot handle incons

56、istent knowledge.This is a real disadvantage because, in business, few things hold true all the time because of the variability in human performance.Second, expert systems cannot apply the judgment and intuition that are important ingredients when solving semistructured or unstructured problems.

57、 人工智能與專家系統(tǒng) 1．AI（人工智能）發(fā)展史 僅僅在通用電器公司開始將電腦應(yīng)用于商業(yè)領(lǐng)域之后兩年，即1956年，就出現(xiàn)了人工智能。人工智能這一術(shù)語(yǔ)是由John McCarthy在Ddartmouth大學(xué)的學(xué)術(shù)論壇上提出的。同年，第一個(gè)人工智能計(jì)算程序——Logic Theorist誕生了。Logic Theorist在推理方面的局限促使了研究人員開發(fā)另一個(gè)程序，那就是GPS(通用問題求解程序)。其目的是為了解決各種各樣的問題，其解決問題的能力比前幾代更強(qiáng)。 AI研究仍在繼續(xù)，但與MIS和DDS等計(jì)算機(jī)應(yīng)用相比，研究熱情的減弱使人工智能的研究相對(duì)落后。然而，在研

58、究方面的不斷努力一定會(huì)推動(dòng)計(jì)算機(jī)向人工智能化方向發(fā)展。 2．AI領(lǐng)域 AI現(xiàn)在已經(jīng)以知識(shí)系統(tǒng)的形式應(yīng)用于商業(yè)領(lǐng)域，既利用人類知識(shí)來解決問題。專家系統(tǒng)是最流行的基于知識(shí)的系統(tǒng)，他是應(yīng)用計(jì)算機(jī)程序以啟發(fā)方式替代專家知識(shí)。Heuristic術(shù)語(yǔ)來自希臘eureka，意思是“探索”。因此，啟發(fā)方式是一種良好猜想的規(guī)則。 啟發(fā)式方法并不能保證其結(jié)果如同DSS系統(tǒng)中傳統(tǒng)的算法那樣絕對(duì)化。但是啟發(fā)式方法提供的結(jié)果非常具體 ，以至于能適應(yīng)于大部分情況啟發(fā)式方法允許專家系統(tǒng)能像專家那樣工作，建議用戶如何解決問題。因?yàn)閷＜蚁到y(tǒng)被當(dāng)作顧問，所以，應(yīng)用專家系統(tǒng)就可以被稱為咨詢。

59、 除了專家系統(tǒng)外，AI還包括以下領(lǐng)域：神經(jīng)網(wǎng)絡(luò)系統(tǒng)、感知系統(tǒng)、學(xué)習(xí)系統(tǒng)、機(jī)器人、AI硬件、自然語(yǔ)言處理。注意這些領(lǐng)域有交叉，交叉部分也就意味著這個(gè)領(lǐng)域可以從另一個(gè)領(lǐng)域中收益。 3．專家系統(tǒng)的吸引力 專家系統(tǒng)的概念是建立在專家知識(shí)能夠存儲(chǔ)在計(jì)算機(jī)中并能被其他人應(yīng)用這一假設(shè)的基礎(chǔ)上的。 專家系統(tǒng)作為一種決策支持系統(tǒng)提供了獨(dú)無二的能力。首先，專家系統(tǒng)為管理者提供了超出其能力的決策機(jī)會(huì)。比如，一家新的銀行投資公司可以應(yīng)用先進(jìn)的專家系統(tǒng)幫助他們進(jìn)行選擇、決策。其次，專家系統(tǒng)在得到一個(gè)解決方案的同時(shí)給出一步步的推理。在很多情況下，推理本身比決策的結(jié)果重要的多。

60、 4．專家系統(tǒng)模型 專家系統(tǒng)模型主要由4個(gè)部分組成：用戶界面使得用戶能與專家系統(tǒng)對(duì)話； 知識(shí)庫(kù)收藏了要特殊解決的問題； 推理引擎提供了解釋知識(shí)庫(kù)的能力； 專家和工程師利用開發(fā)引擎建立專家系統(tǒng)。 1．用戶界面 用戶界面能夠方便管理者向?qū)＜蚁到y(tǒng)中輸入命令、信息，并接受專家系統(tǒng)的輸出。命令中有具體化的參數(shù)設(shè)置，引導(dǎo)專家系統(tǒng)的推理過程。信息以參數(shù)形式賦予某些變量。 （1）專家系統(tǒng)輸入 現(xiàn)在流行的界面格式是圖形化用戶界面格式，這種界面與Windows有些相同的特征。有些系統(tǒng)采用了與所要解決問題相稱的個(gè)性化界面例如，屏幕可能會(huì)顯示機(jī)械

61、裝配圖。 （2）專家系統(tǒng)輸出 專家系統(tǒng)一般是提供解決方案的。這些解決方案都是以如下兩種方始輸出的： ①解決方案解釋。在專家系統(tǒng)提供了問題解決方案后，管理者可能還想知道是如何得到這種方案的。專家系統(tǒng)就會(huì)顯示一步步到達(dá)結(jié)果的推理過程。 ②問題解釋。管理者可能希望得到專家系統(tǒng)對(duì)問題的推理過程。專家系統(tǒng)可能還需要管理者輸入一些信息。管理者問為什么需要信息，然后專家系統(tǒng)就會(huì)提供解釋。 雖然專家系統(tǒng)的內(nèi)部工作很復(fù)雜，但是用戶界面相當(dāng)友好，方便使用。一個(gè)會(huì)用計(jì)算機(jī)的管理者，使用專家系統(tǒng)對(duì)他來說也肯定沒有問題。 2．知識(shí)庫(kù) 知識(shí)庫(kù)即包括描述

62、問題域，也包括以一定的邏輯描述事實(shí)的表示技術(shù)。術(shù)語(yǔ)“問題域”描述了所解決問題的業(yè)務(wù)領(lǐng)域。 （1）規(guī)則 規(guī)則是比較常用的表示技術(shù)。規(guī)則具體規(guī)定了在一種特定的情況下做什么。他有兩部分組成：一是條件，有真和假； 二是方法，是指在條件為真的條件下采取的行動(dòng)。以下是規(guī)則的一個(gè)例子： IF ECONOMIC.INDEX>1.20ANDSEASONAL.INDEX>1.30 THEN SALES.OUTLOOK=”EXCELLENT” 包含在專家系統(tǒng)里的所有規(guī)則叫做規(guī)則集每個(gè)專家系統(tǒng)； 每個(gè)專家系統(tǒng)里的規(guī)則集數(shù)量是不一樣的。一個(gè)簡(jiǎn)單的專家系統(tǒng)有幾十條規(guī)則，

63、復(fù)雜的專家系統(tǒng)有500或1 000甚至10 000條規(guī)則。 （2）規(guī)則網(wǎng)絡(luò) 規(guī)則集里的規(guī)則再物理上并沒有聯(lián)系。但是他在邏輯上的關(guān)系可用層次圖表示最底層的規(guī)則為上一級(jí)提供了依據(jù)。這些依據(jù)有助于上層的規(guī)則得出結(jié)論。 最頂層的可能只包含一個(gè)結(jié)論，這說明只有一個(gè)解決方案。目標(biāo)變量是用來描述解決方案的。他可以是一個(gè)計(jì)算值一個(gè)可識(shí)目標(biāo)，一種措施，或者一些建議。例如，如果一個(gè)專家系統(tǒng)是用來給管理者在是否要進(jìn)入一個(gè)新市場(chǎng)決策上提供建議的，那么，單目標(biāo)變量MARKET.DECISION的值就是Yes或No。 當(dāng)然，也有可能在最高層得到多個(gè)結(jié)論，也就意味著有多種解決方案。

64、例如，在關(guān)于提高市場(chǎng)競(jìng)爭(zhēng)力戰(zhàn)略決策中，專家系統(tǒng)可能就會(huì)提供所有可能的方案，如提高公司產(chǎn)品質(zhì)量、增加廣告投入量或降低價(jià)格。 3．推理引擎 推理引擎是專家系統(tǒng)的一部分，他根據(jù)特定順序在知識(shí)庫(kù)內(nèi)容的基礎(chǔ)上進(jìn)行推理。 在咨詢階段，推理引擎挨個(gè)檢查知識(shí)庫(kù)規(guī)則，當(dāng)某條規(guī)則的條件為真時(shí)就采取規(guī)定的行動(dòng)。在專家系統(tǒng)中，當(dāng)采取行動(dòng)時(shí)，就稱規(guī)則被激活。 在檢查規(guī)則中，一般采用以下兩種方法：正向推理和反向推理。 （1） 正向推理 在正向推理（也稱為正向連接）中，規(guī)則是按照一定順序逐個(gè)檢查的。這種順序可能是輸入到規(guī)則集中的順序，也可能是由用戶自己定義的順序。當(dāng)檢

65、查每個(gè)規(guī)則之后，專家系統(tǒng)開始求值，既為“真”還是為“假”。 規(guī)則求值。當(dāng)條件為真時(shí)，規(guī)則就被激活，然后再檢查下一個(gè)規(guī)則。當(dāng)然還存在規(guī)則的值即非“真”又非“假”的情況。這種情況下，規(guī)則的條件是不知到的，這是，規(guī)則不被取消，繼續(xù)檢查下一條規(guī)則。 迭代推理過程。挨個(gè)檢查規(guī)則集中的規(guī)則，直到規(guī)則集中所有的規(guī)則都檢查完畢。有時(shí)為了設(shè)定一個(gè)目標(biāo)變量值往往要通過好幾輪測(cè)試?？赡軠y(cè)試這個(gè)規(guī)則所需要的信息是來自另一個(gè)規(guī)則測(cè)試的結(jié)果。比如，在第11個(gè)規(guī)則被激活后，第5個(gè)規(guī)則才進(jìn)行測(cè)試。只要有規(guī)則被激活了，測(cè)試就繼續(xù)，直到規(guī)則沒有激活推理過程才結(jié)束。 （2） 反向推理 在反

66、向推理（也稱為反向連接）中，推理引擎將規(guī)則視為一個(gè)待解決的問題。如圖20-1所視的規(guī)則集中，規(guī)則12是一個(gè)問題，因?yàn)樗峙淞艘粋€(gè)值給目標(biāo)變量P 。推理引擎試圖得出規(guī)則12的值，但是，有圖中可知，我們必須先要知道規(guī)則10和11的結(jié)果。規(guī)則10和11是規(guī)則12的子問題。推理引擎先要對(duì)子問題進(jìn)行求值。 圖20-1 規(guī)則集 選擇第一條邏輯路徑。我們假設(shè)當(dāng)前規(guī)則10是待解決的問題。推理引擎在解決問題前首先要確定規(guī)則7和8的值?，F(xiàn)在規(guī)則7和8是子問題，同樣要解決這個(gè)子問題，先要用之前講過的方法細(xì)分問題域，直到能夠求值。 選擇下一條邏輯路徑。當(dāng)專家系統(tǒng)嘗試對(duì)規(guī)則11求值時(shí)，規(guī)則9成為問題。利用規(guī)則4和5的結(jié)果來對(duì)其求值。因?yàn)橐?guī)則4和5都為真，所以規(guī)則9的值也為真。沒有必要對(duì)規(guī)則6進(jìn)行求值了。 規(guī)則9被激活后。規(guī)則11也被激活了。因?yàn)橹灰?guī)則10或規(guī)則11其中一個(gè)為真，就可以激活規(guī)則12了，目標(biāo)變量P的值也就可以得知。 （3） 正向推理和反向推理的比較 反向推理比正向推理要快。因?yàn)榉聪蛲评聿槐乜紤]所有的規(guī)則，也不用一輪一輪在規(guī)則中求值。反向推理