Ch8Expert System

1. Ch8Expert System Dr. Bernard Chen Ph.D. University of Central Arkansas Spring 2011

2. Outline • Expert System introduction • Rule-Based Expert System • Goal Driven Approach • Data Driven Approach • Model-Based Expert System

3. Expert System Introduction • Human experts are able to perform at a successful level because they know 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 area • As with skilled humans, expert systems tend to be specialists, focusing on a narrow set of problems

4. Expert System Introduction • Because of their heuristic, knowledge intensive nature, expert systems generally: • Support inspection of their reasoning processes • Allow easy modification in adding and deleting skills from knowledge base • Reason heuristically, using knowledge to get useful solutions

5. Expert System Introduction • Expert systems are built to solve a wide range of problems in domain such as medicine, math, engineering, chemistry, geology, computer science, business, low, defense and education • These programs address a variety of problems, the following list is a summary of general expert system problem categories:

6. Expert System Introduction • Interpretation --- forming high-level conclusions from collections of raw data • Prediction --- projecting probable consequences of given situations • Diagnosis --- determining the cause of malfunctions based on observable symptoms

7. Expert System Introduction • Design --- finding a configuration of system components 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 constrains

8. The Design of Rule-Based Expert System • architecture of a typical expert system for a particular problem domain.

9. The Design of Rule-Based Expert System • The hear of the expert system is the knowledge base, which contains the knowledge of a particular application domain • In a rule-based expert system, this knowledge is most often represented in the form of if…then… • In the figure, the knowledge base contains both general and case-specific information

10. The Design of Rule-Based Expert System • The inference engine applies the knowledge to the solution of actual problems • It is important to maintain this separation of the knowledge and inference engine because: • Makes it possible to represent knowledge in a more natural fashion • Expert system builder can focus on capturing and organizing problem-solving knowledge than the details of code implementation • Allow change to be made easily • Allows the same control and interface software to be used in different systems

11. Selecting a problem • Expert System involve a considerable investment of money and human effort • Researchers 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 system • Human expertise is not available in all situation where it is needed

12. Selecting a problem • The problem domain is well structured and does not require common sense reasoning • The problem may not be solved using traditional computing methods • Cooperative and articulate experts exist • The problem is proper size and scope

13. NASA Example • NASA has supported its presence in space by developing a fleet of intelligent space probes that autonomously explore the solar system • To 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 during it remaining flight

14. NASA Example • Finally, 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 autonomous system

15. NASA Example • A long-held vision of model-based reasoning has been to use a single centralized model to support a variety of engineering tasks • The tasks include • keeping-track of developing plans • Confirming hardware modes • Reconfiguring hardware • Detecting anomalies • Diagnosis • Fault recovery

16. NASA Example

17. NASA Example • It consist of • a helium tank • Regulators • Propellant tanks • A pair of main engine • Latch valves • Pyro valves

18. NASA Example • The helium tank pressurizes the two propellant tanks, with the regulators acting to reduce the high helium pressure • When propellant path to a main engine are open, the pressurized tank forces fuel and oxidizer into the main engine to produce thrust • The 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

19. NASA Example • Thrust can be provided by either of the main engines and there are a number of ways of opening propellant paths to either main engine

20. NASA Example • Suppose the main engine subsystem has been configured to provide thrust from the left engine by opening the latch valves leading to it • And suppose this engine fails (overheating), so that is fails to provide the required thrust • To 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

21. Selecting a problem • The primary people involved in building an expert system are the knowledge engineer, domain expert, and end user • The 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

22. Exploratory development cycle

23. Exploratory development cycle • It 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 problem • Another major feature of expert system is that the program need never be considered “finished”

24. Outline • Expert System introduction • Rule-Based Expert System • Goal Driven Approach • Data Driven Approach • Model-Based Expert System

25. Strategies for state space search • In 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 hope!!) it generates a path that satisfies the goal condition

26. “tic-tac-toe”state space graph

27. Strategies for state space search • An alternative approach (Goal Driven) is start with the goal that we want to solve • See what rules can generate this goal and determine what conditions must be true to use them • These conditions become the new goals • Working backward through successive subgoals until (we hope again!) it work back to

28. Rule-Based Expert System • Rule based expert system represent problem-solving knowledge as if…then… • It is one of the oldest techniques for representing domain knowledge in an expert system • It is also one of the most natural and widely used in practical and experimental expert system

29. Rule-Based Expert System • In 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 premises in the working memory • This corresponds to a decomposition of the problems’ goal into simpler subgoals • The process continues in the next iteration of the production system, with these premises becoming the new goals to match

30. A unreal Expert System Example Rule 1: if the engine is getting gas, and the engine will turn over, then the problem is spark plugs. Rule 2: if the engine does not turn over, and the lights do not come on then the problem is battery or cables. Rule 3: if the engine does not turn over, and the lights do come on then the problem is the starter motor. Rule 4: if there is gas in the fuel tank, and there is gas in the carburetor then the engine is getting gas.

31. The production system at the start of a consultation in the car diagnostic example.

32. 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 3 • If we resolve conflicts in favor of the lowest-numbered rule, then rule 1 will fire • This cause X to be bound to the value spark plugs and the premises of rule 1 to be placed in the working memory

33. The production system after Rule 1 has fired.

34. The production system after Rule 1 has fired. • Note that there are two premises to rule 1, both of which must be satisfied to prove the conclusion true • So now we need to find out whether • The engine is getting gas, and • The engine will turn over • We may then fire rule 4 for whether “The engine is getting gas”

35. The system after Rule 4 has fired. Note the stack-based approach to goal reduction.

36. The and/or graph searched in thecar diagnosis example, with the conclusion of Rule 4 matching the first premise of Rule 1.

37. Explanation and Transparency in Goal-Driven Reasoning • The following dialogue begins with the computer asking the user about the goals present in the working memory: • Gas in fuel tank? YES • Gas in carburetor? YES • Engine will turn over? WHY

38. Explanation and Transparency in Goal-Driven Reasoning • In 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 the production system is attempting to fire • HOW means “How did you get the result” • The answer is the sequence of rules that were used to conclude a goal

39. Explanation and Transparency in Goal-Driven Reasoning • The following dialogue begins with the computer asking the user about the goals present in the working memory: • Gas in fuel tank? YES • Gas in carburetor? YES • Engine will turn over? WHY It 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.”

40. Explanation and Transparency in Goal-Driven Reasoning Gas in fuel tank? Yes Gas in carburetor? Yes Engine will turn over? Why It 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 gas This follows from rule 4: if gas in fuel tank, and gas in carburetor then engine is getting gas. gas in fuel tank was given by the user gas in carburetor was given by the user

41. Outline • Expert System introduction • Rule-Based Expert System • Goal Driven Approach • Data Driven Approach • Model-Based Expert System

42. Data-Driven Reasoning • The previous example exhibits goal-driven search. The search was also depth-first search • Breadth-first search is more common in Data Driven reasoning • The 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

43. Data-Driven Reasoning • If a piece of 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 1

44. A unreal Expert System Example Rule 1: if (not askable) the engine is getting gas, and the engine will turn over, then the problem is spark plugs. Rule 2: if the engine does not turn over, and the lights do not come on then the problem is battery or cables. Rule 3: if the engine does not turn over, and the lights do come on then the problem is the starter motor. Rule 4: if there is gas in the fuel tank, and there is gas in the carburetor then the engine is getting gas.

45. Data-Driven Reasoning

46. Data-Driven Reasoning • The premise, the engine is getting gas is NOT askable, so rule 1 fails and continue to rule 2 • The engine does not turn over is askable • Suppose the answer to this query is false, so “the engine will turn over” is placed in working memory

47. The production system after evaluating the first premise of Rule 2, which then fails.

48. 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 3 • Where rule 3 also fails • So finally, we move to rule 4

49. The data-driven production system after considering Rule 4, beginning its second pass through the rules.

50. 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 the new contents of working memory, we consider the rules in order for the second round