Expert System.ppt

1、Expert System (ES),Outline,Introduction History of Expert Systems Basic Concepts of Expert Systems Categories of Expert Systems Rule Based Expert Systems Expert Systems Development Benefits, Problems and Limitations of Expert Systems Other Types of Expert Systems,Introduction,Attempt to Imitate Expe

2、rt Reasoning Processes and Knowledge in Solving Specific Problems Most Popular Applied AI Technology Enhance Productivity Augment Work Forces Narrow Problem-Solving Areas or Tasks,Introduction,Provide Direct Application of Expertise Expert Systems Do Not Replace Experts, But They Make their Knowledg

3、e and Experience More Widely Available Permit Nonexperts to Work Better,History of Expert Systems,General Purpose Problem Solver (mid-1960s) A. Newell and H.Simon,few laws of reasoning + powerful computers = expertise performance,desired goal,initial situation set of operators,Early Expert Systems (

4、1970s) DENDRAL infers molecular structure from the unknown compounds MYCIN medical diagnosing (bacterial infections of the blood) E.Feigenbaum (Stanford University) The expert knowledge provides the key to expert performance, while the knowledge representation and inference schemes provide the mecha

5、nism for its use.,History of Expert Systems,Conclusions,General problem solvers are too weak. Human problem solvers are good only if they operate in a very narrow domain. Expert systems must be constantly updated with new information. The complexity of problems requires a considerable amount of know

6、ledge about the problem area.,History of Expert Systems,Development of Commercial Expert Systems (1980) XCON - VAX System configuration (Digital Equipment Corporation) YES/MVS - helps the computer operator monitor and controlthe MVS operating system (IBM) CATS-1 (Delta) - helps diagnose and repair m

7、alfunctions in diesel electric locomotives (General Electric Company) Expert systems development tools EMYCIN, EXPERT, META-DENDRAL, OPS5, ROSIE, CRYSTAL, GOLDWORKS II, etc.,Basic Concepts of Expert Systems,Expertise is the extensive, task-specific knowledge acquired from training, reading, and expe

8、rience. Includes: theories about the problem area rules and procedures heuristics (rules of experience that characterise expert-level decision making in the field) global strategies meta-knowledge facts,Basic Concepts of Expert Systems,Some Facts about Expertise Expertise is usually associated with

9、a high degree of intelligence, but not always with the smartest person Expertise is usually associated with a vast quantity of knowledge Experts learn from past successes and mistakes Expert knowledge is well-stored, organized and retrievable quickly from an expert Experts have excellent recall,Basi

10、c Concepts of Expert Systems,Experts Degrees or levels of expertise Nonexperts outnumber experts often by 100 to 1 Human Expert Behaviors Recognize and formulate the problem Solve problems quickly and properly Explain the solution Learn from experience Restructure knowledge Break rules Determine rel

11、evance Degrade gracefully,Basic Concepts of Expert Systems,An expert system is a computer program that uses knowledge and inference procedures of an expert to solve problems. An ES is a computing system capable of representing and reasoning about some knowledge rich domain, which usually requires a

12、human expert, with a view toward solving problems and/or giving advice. The level of performance makes it “expert” Some also require it to be capable of explaining its reasoning. Does not have a psychological model of how the expert thinks, but a model of the experts model of the domain.,Categories

13、of Expert Systems,interpretation - inferring situation descriptions from sensor data prediction - inferring likely consequences of given situations diagnosis - inferring malfunctions from observations Repair - Administer a prescribed remedy Debugging - Prescribing remedies for malfunctions design -

14、configuring objects under constraints planning - designing actions monitoring - comparing observations to expected outcomes control - governing overall system behaviour instruction - diagnosing, prescribing and guiding users behaviour,Transferring Expertise,Objective of an expert system To transfer

15、expertise from an expert to a computer system and Then on to other humans (nonexperts) Activities Knowledge acquisition Knowledge representation Knowledge inferencing Knowledge transfer to the user Knowledge is stored in a knowledge base,Rule Based Expert Systems,Two Knowledge Types Facts Procedures

16、 (usually rules) IF-THEN-ELSE Regarding the Problem Domain Inferencing Reasoning (Thinking) The computer is programmed so that it can make inferences Performed by the Inference Engine Explanation Capability By the justifier, or explanation subsystem,Basic Architecture of Rule Based Expert Systems,Ba

17、sic Architecture of Rule Based Expert Systems,knowledge base Contains the domain knowledge useful for understanding, formulating, and solving problems. Two Basic Knowledge Base Elements Facts Special heuristics, or rules that direct the use of knowledge( Meta knowledge, 元知識) Knowledge is the primary

18、 raw material of ES Incorporated knowledge representation,Basic Architecture of Rule Based Expert Systems,Inference engine The brain of the ES The control structure (rule interpreter) Provides methodology for reasoning Carries out the reasoning whereby the expert system reaches a solution. It links

19、the rules given in the knowledge base with the facts provided in the database Inference Engine Major Elements Interpreter(執(zhí)行器) Scheduler(調(diào)度器) Consistency Enforcer(協(xié)調(diào)器),Basic Architecture of Rule Based Expert Systems,Global database Blackboard, Workplace Includes a set of facts Area of working memory

20、 to Describe the current problem Record Intermediate results Records Intermediate Hypotheses and Decisions Plan(計劃) Agenda(議程) Solution(中間解),Basic Architecture of Rule Based Expert Systems,Explanation subsystem A part of the expert system that allows a user or decision maker to understand how the ex

21、pert system arrived at certain conclusions or results Enable the user to ask the expert system how a particular conclusion is reached and why a specific fact is needed. An expert system must be able to explain its reasoning and justify its advice, analysis or conclusion For User: increases confidenc

22、e, increases transparance (legal domain!) For Human Expert/Knowledge Engineer:see how knowledge is used, debug For Programmer:Debug, Test, Improve For trainee:have better understanding of expertise,Basic Architecture of Rule Based Expert Systems,Knowledge Acquisition Subsystem Get experts knowledge

23、from resources Provides a convenient and efficient means of capturing and storing all components of the knowledge base,Basic Architecture of Rule Based Expert Systems,User interface Language processor for friendly, problem-oriented communication Is the means of communication between a user seeking a

24、 solution to the problem and an expert system Permits the user to communicate with the system in a more natural way by permitting the the use of simple selection menus restricted natural language Graphic interface,Complete structure of a rule-based expert system,Characteristics of an expert system,A

25、n expert system is built to perform at a human expert level in a narrow, specialised domain. Uses knowledge rather than data for control the solution. Thus, the most important characteristic of an expert system is its high-quality performance. No matter how fast the system can solve a problem, the u

26、ser will not be satisfied if the result is wrong. On the other hand, the speed of reaching a solution is very important. Even the most accurate decision or diagnosis may not be useful if it is too late to apply for instance, in an emergency, when a patient dies or a nuclear power plant explodes.,Cha

27、racteristics of an expert system,Expert systems apply heuristics to guide the reasoning thus reduce the search area for a solution. A unique feature of an expert system is its explanation capability. It enables the expert system to review its own reasoning and explain its decisions. Expert systems e

28、mploy symbolic reasoning when solving a problem. Symbols are used to represent different types of knowledge such as facts, concepts and rules.,Characteristics of an expert system,In expert systems, knowledge is separated from its processing. A conventional program is a mixture of knowledge and the c

29、ontrol structure to process this knowledge. This mixing leads to difficulties in understanding and reviewing the program code, as any change to the code affects both the knowledge and its processing. Uses meta-knowledge. When an expert system shell is used, a knowledge engineer or an expert simply e

30、nters rules in the knowledge base. Each new rule adds some new knowledge and makes the expert system smarter.,Can expert systems make mistakes?,Even a brilliant expert is only a human and thus can make mistakes. This suggests that an expert system built to perform at a human expert level also should

31、 be allowed to make mistakes. But we still trust experts, even we recognise that their judgements are sometimes wrong. Likewise, at least in most cases, we can rely on solutions provided by expert systems, but mistakes are possible and we should be aware of this.,Comparison of expert systems with co

32、nventional systems and human experts,Comparison of expert systems with conventional systems and human experts,Expert Systems Development,Expert system building is a software engineering task, but with a twist: knowledge base KB will be continually refined, corrected, updated expert system design ref

33、ine errors when found,The Human Element in Expert Systems,Expert Knowledge Engineer(知識工程師) User Others,The Human Element in Expert Systems,Domain expert Has the special knowledge, judgment, experience and methods to give advice and solve problems Provides knowledge about task performance Knowledge e

34、ngineer Helps the expert(s) structure the problem area by interpreting and integrating human answers to questions, drawing analogies, posing counterexamples, and bringing to light conceptual difficulties Usually also the System Builder,The Human Element in Expert Systems,Knowledge user Possible Clas

35、ses of Users A non-expert client seeking direct advice (ES acts as a Consultant or Advisor) A student who wants to learn (Instructor) An ES builder improving or increasing the knowledge base (Partner) An expert (Colleague or Assistant) The Expert and the Knowledge Engineer Should Anticipate Users Ne

36、eds and Limitations When Designing ES,Other Participants,System Builder Systems Analyst Tool Builder Vendors Support Staff Network Expert,How Expert Systems Work,Major Activities of ES Construction and Use Development Consultation Improvement,Expert Systems Development,Determining requirements,Ident

37、ifying experts,Construct expert system components,Implementing results,Maintaining and reviewing system,Domain The area of knowledgeaddressed by theexpert system.,Expert Systems Development,Knowledge base development Knowledge separated into Declarative (factual) knowledge and Procedural knowledge D

38、evelopment (or Acquisition) of an inference engine, blackboard, explanation facility, or any other software Determine knowledge representations,Tools for Building Expert Systems,Skeletal systems , ES Shell(骨架型，外殼) the most frequent choice for commercial systems in recent years Programming languages(

39、語言型) conventional programming languages (e.g. Pascal, C+, Java) artificial intelligence programming languages (particularly LISP and Prolog) Assistant tools(輔助工具) Expert System Support Environment (支撐環(huán)境),ES Shell,Includes All Generic ES Components But No Knowledge EMYCIN from MYCIN (E=Empty) Expert

40、Systems Shells Software Development Packages Exsys () Demo InstantTea K-Vision KnowledgePro,Consultation,Deploy ES to Users (Typically Novices) ES Must be Very Easy to Use ES Improvement By Rapid Prototyping(快速原型法),Expert Systems Benefits,Increased Output and Productivity Decreased Decision Making Time Increased Process(es) and Product Quality Reduced Downtime Capture Scarce Expertise Flexibility Easier Equipment Operation