Ch8-Expert-System---Department-of-Computer-Science8专家系统-计算机科学系课件

1、Ch8Expert SystemDr. Bernard Chen Ph.D.University of Central ArkansasSpring 2019Outline Expert System introductionRule-Based Expert SystemGoal Driven ApproachData Driven ApproachModel-Based Expert SystemExpert System Introduction Human experts are able to perform at a successful level because they kn

2、ow a lot about their areas of expertise An Expert System use knowledge specific to a problem domain to provide “expert quality” performance in that application areaAs with skilled humans, expert systems tend to be specialists, focusing on a narrow set of problemsExpert System IntroductionBecause of

3、their heuristic, knowledge intensive nature, expert systems generally:Support inspection of their reasoning processes Allow easy modification in adding and deleting skills from knowledge baseReason heuristically, using knowledge to get useful solutionsExpert System IntroductionExpert systems are bui

4、lt to solve a wide range of problems in domain such as medicine, math, engineering, chemistry, geology, computer science, business, low, defense and educationThese programs address a variety of problems, the following list is a summary of general expert system problem categories: Expert System Intro

5、ductionInterpretation - forming high-level conclusions from collections of raw dataPrediction - projecting probable consequences of given situationsDiagnosis - determining the cause of malfunctions based on observable symptoms Expert System IntroductionDesign - finding a configuration of system comp

6、onents that meets performance goals while satisfying a set of design constrains Planning - devising a sequence of actions that will achieve a set of goals given starting conditions and runtime constrainsThe Design of Rule-Based Expert Systemarchitecture of a typical expert system for a particular pr

7、oblem domain.The Design of Rule-Based Expert SystemThe hear of the expert system is the knowledge base, which contains the knowledge of a particular application domainIn a rule-based expert system, this knowledge is most often represented in the form of ifthenIn the figure, the knowledge base contai

8、ns both general and case-specific informationThe Design of Rule-Based Expert SystemThe inference engine applies the knowledge to the solution of actual problemsIt is important to maintain this separation of the knowledge and inference engine because:Makes it possible to represent knowledge in a more

9、 natural fashionExpert system builder can focus on capturing and organizing problem-solving knowledge than the details of code implementationAllow change to be made easilyAllows the same control and interface software to be used in different systemsSelecting a problemExpert System involve a consider

10、able investment of money and human effortResearchers have developed guidelines to determine whether a problem is appropriate for expert system solution:The need for the solution justifies the cost and efforts of building an expert systemHuman expertise is not available in all situation where it is n

11、eededSelecting a problemThe problem domain is well structured and does not require common sense reasoningThe problem may not be solved using traditional computing methodsCooperative and articulate experts existThe problem is proper size and scope NASA ExampleNASA has supported its presence in space

12、by developing a fleet of intelligent space probes that autonomously explore the solar systemTo achieve success through years in the harsh conditions of space travel, a craft needs to be able to radically reconfigure its control regime in response to failures and then plan around these failures durin

13、g it remaining flightNASA ExampleFinally, NASA expects that the set of potential failure scenarios and possible responses will be much too large to use software that supports preflight enumeration of all contingencies Livingstone is an implemented kernel for a model-based reactive self-configuring a

14、utonomous systemNASA ExampleA long-held vision of model-based reasoning has been to use a single centralized model to support a variety of engineering tasksThe tasks include keeping-track of developing plansConfirming hardware modesReconfiguring hardwareDetecting anomaliesDiagnosisFault recoveryNASA

15、 ExampleNASA ExampleIt consist of a helium tankRegulatorsPropellant tanksA pair of main engine Latch valvesPyro valvesNASA ExampleThe helium tank pressurizes the two propellant tanks, with the regulators acting to reduce the high helium pressureWhen propellant path to a main engine are open, the pre

16、ssurized tank forces fuel and oxidizer into the main engine to produce thrustThe pyro valve is to isolate parts of the main engine subsystem until they are needed, or to permanently isolate failed components The latch valve are controlled using valve drivers and the accelerometer NASA ExampleThrust

17、can be provided by either of the main engines and there are a number of ways of opening propellant paths to either main engine NASA ExampleSuppose the main engine subsystem has been configured to provide thrust from the left engine by opening the latch valves leading to itAnd suppose this engine fai

18、ls (overheating), so that is fails to provide the required thrustTo ensure that the desire thrust is provided, the spacecraft must be transitioned to a new configuration in which thrust is now provided by the main engine on the right side Selecting a problemThe primary people involved in building an

19、 expert system are the knowledge engineer, domain expert, and end userThe domain expert is primarily responsible for spelling out skills to knowledge engineer It is often useful for knowledge engineer to be a novice in the problem domain Exploratory development cycle Exploratory development cycleIt

20、is also understood that the prototype may be thrown away if it becomes to cumbersome or if the designers decide to change their basic approach to the problemAnother major feature of expert system is that the program need never be considered “finished”Outline Expert System introductionRule-Based Expe

21、rt SystemGoal Driven ApproachData Driven ApproachModel-Based Expert SystemStrategies for state space searchIn data driven search, also called forward chaining, the problem solver begins with the given facts of the problem and set of legal moves for changing state This process continues until (we hop

22、e!) it generates a path that satisfies the goal condition “tic-tac-toe” state space graph Strategies for state space searchAn alternative approach (Goal Driven) is start with the goal that we want to solveSee what rules can generate this goal and determine what conditions must be true to use themThe

23、se conditions become the new goalsWorking backward through successive subgoals until (we hope again!) it work back toRule-Based Expert SystemRule based expert system represent problem-solving knowledge as ifthenIt is one of the oldest techniques for representing domain knowledge in an expert systemI

24、t is also one of the most natural and widely used in practical and experimental expert systemRule-Based Expert SystemIn a goal-driven expert system, the goal expression is initially placed in working memory The system matches rule conclusions with the goal, selecting one rule and placing its premise

25、s in the working memoryThis corresponds to a decomposition of the problems goal into simpler subgoalsThe process continues in the next iteration of the production system, with these premises becoming the new goals to matchA unreal Expert System Example Rule 1:if the engine is getting gas, andthe eng

26、ine will turn over,thenthe problem is spark plugs.Rule 2:ifthe engine does not turn over, andthe lights do not come onthenthe problem is battery or cables.Rule 3:ifthe engine does not turn over, andthe lights do come onthen the problem is the starter motor.Rule 4:ifthere is gas in the fuel tank, and

27、there is gas in the carburetorthenthe engine is getting gas.The production system at the start of a consultation in the car diagnostic example. The production system at the start of a consultation in the car diagnostic example.Three rules match with this expression in working memory: rule 1, 2, and

28、3If we resolve conflicts in favor of the lowest-numbered rule, then rule 1 will fireThis cause X to be bound to the value spark plugs and the premises of rule 1 to be placed in the working memoryThe production system after Rule 1 has fired. The production system after Rule 1 has fired.Note that ther

29、e are two premises to rule 1, both of which must be satisfied to prove the conclusion trueSo now we need to find out whetherThe engine is getting gas, and The engine will turn overWe may then fire rule 4 for whether “The engine is getting gas”The system after Rule 4 has fired. Note the stack-based a

30、pproach to goal reduction. The and/or graph searched in the car diagnosis example, with the conclusion of Rule 4 matching the first premise of Rule 1. Explanation and Transparency in Goal-Driven ReasoningThe following dialogue begins with the computer asking the user about the goals present in the w

31、orking memory:Gas in fuel tank?YESGas in carburetor?YESEngine will turn over?WHYExplanation and Transparency in Goal-Driven ReasoningIn general, the two questions answered by rule-based expert system are WHY? and HOW?WHY means “why did you ask for that information”The answer is the current rule that

32、 the production system is attempting to fireHOW means “How did you get the result”The answer is the sequence of rules that were used to conclude a goal Explanation and Transparency in Goal-Driven ReasoningThe following dialogue begins with the computer asking the user about the goals present in the

33、working memory:Gas in fuel tank?YESGas in carburetor?YESEngine will turn over?WHYIt has been established that:1. The engine is getting gas, 2. The engine will turn over, (we need to know)So that we can make the conclusion that “Then the problem is the spark plugs.”Explanation and Transparency in Goa

34、l-Driven ReasoningGas in fuel tank?YesGas in carburetor?YesEngine will turn over?WhyIt has been established that:1. The engine is getting gas, 2. The engine will turn over,Then the problem is the spark plugs.How the engine is getting gasThis follows from rule 4:ifgas in fuel tank, andgas in carburet

35、orthenengine is getting gas.gas in fuel tank was given by the usergas in carburetor was given by the userOutline Expert System introductionRule-Based Expert SystemGoal Driven ApproachData Driven ApproachModel-Based Expert SystemData-Driven ReasoningThe previous example exhibits goal-driven search. T

36、he search was also depth-first searchBreadth-first search is more common in Data Driven reasoningThe algorithm for this category is simple: compare the contents of working memory with the conditions of each rule in the rule base according to the order of the rules Data-Driven ReasoningIf a piece of

37、information that makes up the premise of a rule is not the conclusion of some other rule, then that fact will be deemed “askable”For example: the engine is getting gas is not askable in the premise of rule 1A unreal Expert System Example Rule 1:if (not askable) the engine is getting gas, andthe engi

38、ne will turn over,thenthe problem is spark plugs.Rule 2:ifthe engine does not turn over, andthe lights do not come onthenthe problem is battery or cables.Rule 3:ifthe engine does not turn over, andthe lights do come onthen the problem is the starter motor.Rule 4:ifthere is gas in the fuel tank, andt

39、here is gas in the carburetorthenthe engine is getting gas.Data-Driven ReasoningData-Driven ReasoningThe premise, the engine is getting gas is NOT askable, so rule 1 fails and continue to rule 2The engine does not turn over is askableSuppose the answer to this query is false, so “the engine will tur

40、n over” is placed in working memoryThe production system after evaluating the first premise of Rule 2, which then fails. The production system after evaluating the first premise of Rule 2, which then fails.Rule 2 fails, since the first of two AND premises is false, we move to rule 3Where rule 3 also

41、 fails So finally, we move to rule 4The data-driven production system after considering Rule 4, beginning its second pass through the rules. The data-driven production system after considering Rule 4, beginning its second pass through the rules.At this point, all the rules have been considered With

42、the new contents of working memory, we consider the rules in order for the second roundOutline Expert System introductionRule-Based Expert SystemGoal Driven ApproachData Driven ApproachModel-Based Expert SystemModel-Based Expert SystemHuman expertise is an extremely complex combination of:Theoretica

43、l knowledgeExperienced based problem solving heuristicsExample of past problems and their solutions Interpretive skillsThrough years of experience, human expert develop very powerful rules for dealing with commonly encountered situationsThese rules are often highly “complied”Model-Based Expert Syste

44、mIn a rule-based expert system example for semiconductor failure analysis, a descriptive approach might base on:Discoloration of components (burned-out)History of faults in similar devicesObservation of component by electron microscopeHowever, approaches that use rules to link observations and diagnosis do not offer the benefits of a deeper analysis of devices structure and functionModel-Based Expert SystemA more robust, deeply explanatory approach would begin with a detailed model of the physical structure of the circuit and eq