工智专家系统外文文献译文原文
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 Theorist’s 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 knowledgebased 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 consultationthe 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 expert’s 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 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’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 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 userfriendly 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 ECONOMICINDEX>120 AND SEASONALINDEX>130
THEN SALESOUTLOOKEXCELLENT
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 systemand 500 1000 or 10000 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 toplevel management on whether to enter a new market area a value of Yes or Not would be assigned to the singlegoal 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 firm’s 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 rule’s 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 passThe 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 201 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
Figure201 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 reasoningThe 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 readymade 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 within 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 casebased 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 networklike 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 knowledgebase 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 decisionmaking The improvement comes from 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 considered 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 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 human 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 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 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 problemsolving tool First they cannot handle inconsistent 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
工智专家系统
1．AI（工智）发展史
仅仅通电器公司开始电脑应商业领域两年1956年出现工智工智术语John McCarthyDdartmouth学学术坛提出年第工智计算程序——Logic Theorist诞生Logic Theorist推理方面局限促研究员开发程序GPS(通问题求解程序)目解决种样问题解决问题力前代更强
AI研究继续MISDDS等计算机应相研究热情减弱工智研究相落然研究方面断努力定会推动计算机工智化方发展
2．AI领域
AI现已知识系统形式应商业领域利类知识解决问题专家系统流行基知识系统应计算机程序启发方式代专家知识Heuristic术语希腊eureka意思探索启发方式种良猜想规
启发式方法保证结果DSS系统中传统算法样绝化启发式方法提供结果非常具体 适应部分情况启发式方法允许专家系统专家样工作建议户解决问题专家系统作顾问应专家系统称咨询
专家系统外AI包括领域：神网络系统感知系统学系统机器AI硬件然语言处理注意领域交叉交叉部分意味着领域领域中收益
3．专家系统吸引力
专家系统概念建立专家知识够存储计算机中应假设基础
专家系统作种决策支持系统提供独二力首先专家系统理者提供超出力决策机会家新银行投资公司应先进专家系统帮助进行选择决策次专家系统解决方案时出步步推理情况推理身决策结果重
4．专家系统模型
专家系统模型4部分组成：户界面户专家系统话知识库收藏特殊解决问题推理引擎提供解释知识库力专家工程师利开发引擎建立专家系统
1．户界面
户界面够方便理者专家系统中输入命令信息接受专家系统输出命令中具体化参数设置引导专家系统推理程信息参数形式赋予某变量
（1）专家系统输入
现流行界面格式图形化户界面格式种界面Windows相特征系统采解决问题相称性化界面例屏幕会显示机械装配图
（2）专家系统输出
专家系统般提供解决方案解决方案两种方始输出：
①解决方案解释专家系统提供问题解决方案理者想知道种方案专家系统会显示步步达结果推理程
②问题解释理者希专家系统问题推理程专家系统需理者输入信息理者问什需信息然专家系统会提供解释
然专家系统部工作复杂户界面相友方便会计算机理者专家系统说肯定没问题
2．知识库
知识库包括描述问题域包括定逻辑描述事实表示技术术语问题域描述解决问题业务领域
（1）规
规较常表示技术规具体规定种特定情况做什两部分组成：条件真假二方法指条件真条件采取行动规例子：
IF ECONOMICINDEX>120ANDSEASONALINDEX>130
THEN SALESOUTLOOKEXCELLENT
包含专家系统里规做规集专家系统专家系统里规集数量样简单专家系统十条规复杂专家系统5001 000甚10 000条规
（2）规网络
规集里规物理没联系逻辑关系层次图表示底层规级提供助层规出结
顶层包含结说明解决方案目标变量描述解决方案计算值识目标种措施者建议例果专家系统理者否进入新市场决策提供建议单目标变量MARKETDECISION值YesNo
然高层结意味着种解决方案例关提高市场竞争力战略决策中专家系统会提供方案提高公司产品质量增加广告投入量降低价格
3．推理引擎
推理引擎专家系统部分根特定序知识库容基础进行推理
咨询阶段推理引擎挨检查知识库规某条规条件真时采取规定行动专家系统中采取行动时称规激活
检查规中般采两种方法：正推理反推理
（1） 正推理
正推理（称正连接）中规定序逐检查种序输入规集中序户定义序检查规专家系统开始求值真假
规求值条件真时规激活然检查规然存规值非真非假情况种情况规条件知规取消继续检查条规
迭代推理程挨检查规集中规直规集中规检查完毕时设定目标变量值通轮测试测试规需信息规测试结果第11规激活第5规进行测试规激活测试继续直规没激活推理程结束
（2） 反推理
反推理（称反连接）中推理引擎规视解决问题图201视规集中规12问题分配值目标变量P 推理引擎试图出规12值图中知必须先知道规1011结果规1011规12子问题推理引擎先子问题进行求值
图201 规集
选择第条逻辑路径假设前规10解决问题推理引擎解决问题前首先确定规78值现规78子问题样解决子问题先前讲方法细分问题域直够求值
选择条逻辑路径专家系统尝试规11求值时规9成问题利规45结果求值规45真规9值真没必规6进行求值
规9激活规11激活规10规11中真激活规12目标变量P值知
（3） 正推理反推理较
反推理正推理快反推理必考虑规轮轮规中求值反推理尤适种情况：
①目标变量
②规
③求问题结程中须规检查便
推理引擎适合正推理适合反推理视具体情况定
4．开发引擎
专家系统第4重组件开发引擎建造专家系统推理引擎包含许规时建造专家系统程涉建立规集两种基方法：程序语言专家系统外壳程序
（1） 程序语言
应语言创建专家系统适合符号化表示知识库两种语言：LispPologLisp1959年McCarthy（首届AI会议成员）开发Prolog1972年Alain ColmerauerMarseilles学开发
（2） 专家系统外壳程序
第专家系统MycinStanford学Edward ShortliffeStanley Cohen物理学家Stanton Axline帮助开发Mycin诊断某种传染病
成功开发第专家系统Mycin开发者试图领域应成果发现果知识库更换成反映问题相关知识Mycin推理引擎够适该类型问题域种发现开创建立专家系统新方法：专家系统外壳程序段预先编写程序增加相应知识库够适具体问题域应专家系统解决商业问题焦点外壳程序应
问题域导出专家系统外壳程序中例子桌面帮助支持桌面帮助支持系统单元户提供技术帮助信息服务单元典型户信息专家提供桌面帮助桌面帮助基形式两专家户进行电话答疑户提出问题专家予解答
桌面帮助问题普遍致公司成立桌面帮助部门方便话年会重项活动演示专家系统外壳程序桌面帮助公司应中外壳程序时必须扩充相关生产线知识库信息服务单元应该扩充硬件应软件相关数软件帮助库中扩充软件描述等
桌面帮助专家系统应户桌面帮助员工直接专家系统话系统解决问题工智智化程度测试户否判出 机器话种测试称Turing测试Alan Turing计算机学伟先驱
桌面帮助专家系统利信息表示技术较流行方法CBR(casebased reasoning基事实推理)根历史数作识问题基础然提出解决方案系统决策树形式表示网状结构户够回答解决相关问题
专家系统外壳程序引入工智公司没必开发系统商业领域公司常专家系统外壳程序实施基知识系统
5．专家系统优缺点
计算机应样专家系统提供实际利益足处理者公司专家系统中收益
1家系统理者带处
理者应专家系统改进决策改进表现：
（1）提供更选择解决问题程中专家系统促理者考虑更选择没专家系统考虑范围限财务理踪30种股票表现专家系统踪300种股票考虑投资范围扩增加选择佳方案性
（2）应更高逻辑层理者助专家系统够达先进专家逻辑水
（3）倾注更时间评估方案理者够快速专家系统中建议理者行动前留更选择权衡时间
（4）决策更加致理者相计算机会搀杂情感波动素旦推理输入计算机理者会确定方案
2公司带处
专家系统公司带处：
（1）公司更业绩理者助专家系统解决问题公司理机制改善公司够更接目标
（2）保持公司知识控制专家系统老员工传授丰富验新员工创造机会员工离开够知识成体
6专家系统缺点
专家系统两特征限制作商务问题解决工具潜第处理致性知识问题实实足处商业中素变性没事情时时正确第二专家系统应判断指导解决结构化问题时重素
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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 Theorist’s 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 knowledgebased 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 consultationthe 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 expert’s 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 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’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 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 userfriendly 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 ECONOMICINDEX>120 AND SEASONALINDEX>130
THEN SALESOUTLOOKEXCELLENT
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 systemand 500 1000 or 10000 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 toplevel management on whether to enter a new market area a value of Yes or Not would be assigned to the singlegoal 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 firm’s 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 rule’s 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 passThe 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 201 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
Figure201 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 reasoningThe 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 readymade 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 within 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 casebased 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 networklike 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 knowledgebase 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 decisionmaking The improvement comes from 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 considered 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 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 human 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 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 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 problemsolving tool First they cannot handle inconsistent 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
工智专家系统
1．AI（工智）发展史
仅仅通电器公司开始电脑应商业领域两年1956年出现工智工智术语John McCarthyDdartmouth学学术坛提出年第工智计算程序——Logic Theorist诞生Logic Theorist推理方面局限促研究员开发程序GPS(通问题求解程序)目解决种样问题解决问题力前代更强
AI研究继续MISDDS等计算机应相研究热情减弱工智研究相落然研究方面断努力定会推动计算机工智化方发展
2．AI领域
AI现已知识系统形式应商业领域利类知识解决问题专家系统流行基知识系统应计算机程序启发方式代专家知识Heuristic术语希腊eureka意思探索启发方式种良猜想规
启发式方法保证结果DSS系统中传统算法样绝化启发式方法提供结果非常具体 适应部分情况启发式方法允许专家系统专家样工作建议户解决问题专家系统作顾问应专家系统称咨询
专家系统外AI包括领域：神网络系统感知系统学系统机器AI硬件然语言处理注意领域交叉交叉部分意味着领域领域中收益
3．专家系统吸引力
专家系统概念建立专家知识够存储计算机中应假设基础
专家系统作种决策支持系统提供独二力首先专家系统理者提供超出力决策机会家新银行投资公司应先进专家系统帮助进行选择决策次专家系统解决方案时出步步推理情况推理身决策结果重
4．专家系统模型
专家系统模型4部分组成：户界面户专家系统话知识库收藏特殊解决问题推理引擎提供解释知识库力专家工程师利开发引擎建立专家系统
1．户界面
户界面够方便理者专家系统中输入命令信息接受专家系统输出命令中具体化参数设置引导专家系统推理程信息参数形式赋予某变量
（1）专家系统输入
现流行界面格式图形化户界面格式种界面Windows相特征系统采解决问题相称性化界面例屏幕会显示机械装配图
（2）专家系统输出
专家系统般提供解决方案解决方案两种方始输出：
①解决方案解释专家系统提供问题解决方案理者想知道种方案专家系统会显示步步达结果推理程
②问题解释理者希专家系统问题推理程专家系统需理者输入信息理者问什需信息然专家系统会提供解释
然专家系统部工作复杂户界面相友方便会计算机理者专家系统说肯定没问题
2．知识库
知识库包括描述问题域包括定逻辑描述事实表示技术术语问题域描述解决问题业务领域
（1）规
规较常表示技术规具体规定种特定情况做什两部分组成：条件真假二方法指条件真条件采取行动规例子：
IF ECONOMICINDEX>120ANDSEASONALINDEX>130
THEN SALESOUTLOOKEXCELLENT
包含专家系统里规做规集专家系统专家系统里规集数量样简单专家系统十条规复杂专家系统5001 000甚10 000条规
（2）规网络
规集里规物理没联系逻辑关系层次图表示底层规级提供助层规出结
顶层包含结说明解决方案目标变量描述解决方案计算值识目标种措施者建议例果专家系统理者否进入新市场决策提供建议单目标变量MARKETDECISION值YesNo
然高层结意味着种解决方案例关提高市场竞争力战略决策中专家系统会提供方案提高公司产品质量增加广告投入量降低价格
3．推理引擎
推理引擎专家系统部分根特定序知识库容基础进行推理
咨询阶段推理引擎挨检查知识库规某条规条件真时采取规定行动专家系统中采取行动时称规激活
检查规中般采两种方法：正推理反推理
（1） 正推理
正推理（称正连接）中规定序逐检查种序输入规集中序户定义序检查规专家系统开始求值真假
规求值条件真时规激活然检查规然存规值非真非假情况种情况规条件知规取消继续检查条规
迭代推理程挨检查规集中规直规集中规检查完毕时设定目标变量值通轮测试测试规需信息规测试结果第11规激活第5规进行测试规激活测试继续直规没激活推理程结束
（2） 反推理
反推理（称反连接）中推理引擎规视解决问题图201视规集中规12问题分配值目标变量P 推理引擎试图出规12值图中知必须先知道规1011结果规1011规12子问题推理引擎先子问题进行求值
图201 规集
选择第条逻辑路径假设前规10解决问题推理引擎解决问题前首先确定规78值现规78子问题样解决子问题先前讲方法细分问题域直够求值
选择条逻辑路径专家系统尝试规11求值时规9成问题利规45结果求值规45真规9值真没必规6进行求值
规9激活规11激活规10规11中真激活规12目标变量P值知
（3） 正推理反推理较
反推理正推理快反推理必考虑规轮轮规中求值反推理尤适种情况：
①目标变量
②规
③求问题结程中须规检查便
推理引擎适合正推理适合反推理视具体情况定
4．开发引擎
专家系统第4重组件开发引擎建造专家系统推理引擎包含许规时建造专家系统程涉建立规集两种基方法：程序语言专家系统外壳程序
（1） 程序语言
应语言创建专家系统适合符号化表示知识库两种语言：LispPologLisp1959年McCarthy（首届AI会议成员）开发Prolog1972年Alain ColmerauerMarseilles学开发
（2） 专家系统外壳程序
第专家系统MycinStanford学Edward ShortliffeStanley Cohen物理学家Stanton Axline帮助开发Mycin诊断某种传染病
成功开发第专家系统Mycin开发者试图领域应成果发现果知识库更换成反映问题相关知识Mycin推理引擎够适该类型问题域种发现开创建立专家系统新方法：专家系统外壳程序段预先编写程序增加相应知识库够适具体问题域应专家系统解决商业问题焦点外壳程序应
问题域导出专家系统外壳程序中例子桌面帮助支持桌面帮助支持系统单元户提供技术帮助信息服务单元典型户信息专家提供桌面帮助桌面帮助基形式两专家户进行电话答疑户提出问题专家予解答
桌面帮助问题普遍致公司成立桌面帮助部门方便话年会重项活动演示专家系统外壳程序桌面帮助公司应中外壳程序时必须扩充相关生产线知识库信息服务单元应该扩充硬件应软件相关数软件帮助库中扩充软件描述等
桌面帮助专家系统应户桌面帮助员工直接专家系统话系统解决问题工智智化程度测试户否判出 机器话种测试称Turing测试Alan Turing计算机学伟先驱
桌面帮助专家系统利信息表示技术较流行方法CBR(casebased reasoning基事实推理)根历史数作识问题基础然提出解决方案系统决策树形式表示网状结构户够回答解决相关问题
专家系统外壳程序引入工智公司没必开发系统商业领域公司常专家系统外壳程序实施基知识系统
5．专家系统优缺点
计算机应样专家系统提供实际利益足处理者公司专家系统中收益
1家系统理者带处
理者应专家系统改进决策改进表现：
（1）提供更选择解决问题程中专家系统促理者考虑更选择没专家系统考虑范围限财务理踪30种股票表现专家系统踪300种股票考虑投资范围扩增加选择佳方案性
（2）应更高逻辑层理者助专家系统够达先进专家逻辑水
（3）倾注更时间评估方案理者够快速专家系统中建议理者行动前留更选择权衡时间
（4）决策更加致理者相计算机会搀杂情感波动素旦推理输入计算机理者会确定方案
2公司带处
专家系统公司带处：
（1）公司更业绩理者助专家系统解决问题公司理机制改善公司够更接目标
（2）保持公司知识控制专家系统老员工传授丰富验新员工创造机会员工离开够知识成体
6专家系统缺点
专家系统两特征限制作商务问题解决工具潜第处理致性知识问题实实足处商业中素变性没事情时时正确第二专家系统应判断指导解决结构化问题时重素
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