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Expert Systems:

Expert Systems:. Engineering knowledge. Motivations. You saw expert system architecture in the last lecture. Today the focus is on knowledge engineering. There are different types of knowledge. The right approach and technique should be used for the knowledge required. Objectives.

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Expert Systems:

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  1. Expert Systems: Engineering knowledge

  2. Motivations • You saw expert system architecture in the last lecture. • Today the focus is on knowledge engineering. • There are different types of knowledge. • The right approach and technique should be used for the knowledge required.

  3. Objectives • Is this patient eligible for a cancer clinical trial? • What problems are appropriate to be solved by expert system technology? • Expert system development cycle • Knowledge engineering • Car won’t start. What’s wrong? • MYCIN: Antibiotic recommendation

  4. Is this patient eligible for a cancer clinical trial? • Clinical trial eligibility criteria • Female, older than 30 • No prior surgery • Breast cancer, stage II or III Expert system analyzes a patient’s data and determines whether the patient is eligible for Moffitt Cancer Center clinical trials. • Guides a clinician through related questions • Identifies appropriate medical tests • Selects matching clinical trials • Minimizes pain and cost of selection process

  5. Clinical trial eligibility criteria • Female, older than 30 • No prior surgery • Breast cancer, stage II or III AND Age > 30 Prior-surgery = NO OR Cancer-stage = II Cancer-stage = III

  6. Example: Questions Sex: Female Male Patient is not eligible

  7. Example: Questions Sex: Age: Female Male Patient is not eligible 25

  8. Example: Questions Sex: Age: Female Male 35

  9. Example: Questions Cancer stage: Prior surgery? Yes No Unknown I II III IV Patient is eligible

  10. Selecting Problems for ES Guidelines to determine whether a problem is appropriate for expert system solution: • The need for the solution justifies the cost and effort of building an expert system. • Human expertise is not available in all situations where it is needed. • Problem involves symbolic reasoning. • Problem domain is well structured and does not require commonsense reasoning. • Problem may not be solved using traditional computing methods. • Cooperative and articulate experts exist. • The problem is of proper size and scope. Don't attempt too much in one ES.

  11. Expert systemdevelopment cycle Knowledge engineer interviews experts. Learns how they solve problems. Early prototyping Frequent updates and refinement in design

  12. Knowledge acquisition • Acquire expertise (domain knowledge) from domain experts • Characteristics of domain expertise • Intuitive • Difficult to access • Difficult to describe • Free format • Vague, imprecise, and bias • Dynamic (change) • Seem inconsistent at times

  13. Knowledge engineering • Engineering process • Knowledge acquisition • Knowledge representation • System implementation • System maintenance • Conceptual Model • Intermediate representation of domain knowledge • some form of mental model, similar to state spaces, conceptual graphs, etc.

  14. Rule-Based Expert Systems • Features • Knowledge base organized as a set of if … then …rules • Lead to the ES architecture • Natural • Widely used • Production system vs. rule-based ES • Production system: Condition  Action • Rule-based: if Condition then Action • Differences: Production systems implement graph search with either goal-driven or data-driven strategy, while rule-based ES implements logic reasoning

  15. Car won’t start. What’s the problem? Rule 1: ifthe 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. R1.1 R3.1 R1.2 R3.2 R1.3 R3.3 R2.1 R4.1 R2.2 R4.2 R2.3 R4.3

  16. A goal turns into a condition Rule 1: ifthe 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. R1.1 R3.1 R1.2 R3.2 R1.3 R3.3 R2.1 R4.1 R2.2 R4.2 R2.3 R4.3

  17. R1.1 R1.2 R1.3 R4.1 R4.2 R4.3R1.1 R1.2 R1.3 Depth-first reasoning Rule 4 is chosen and placed in the working memory to satisfy R1.1. Rule 1 is chosen and placed in the working memory. Now the problem is to satisfy conditions R4.1, R4.2 and R1.2. From here the ES needs to ask the user some questions to think any further. Now the original problem becomes 2 subproproblems, to satisfy conditions R1.1 and R1.2.

  18. R1.1 not askable ? R4.3 R4.1 R4.2 And/or graph representation Backward chaining Goal-driven Depth-first At this point, the ES needs to query the user before it can proceed. If the answers are yes, then the ES will conclude that the problem is spark plugs. Otherwise, it will ask other questions. Forward chaining Data-driven ? ?

  19. Askable questions If a condition is NOT the conclusion of some rule, then it is askable. R2.1 is askable. R1.1 is not askable. Rule 1: ifthe 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. R1.1 R3.1 R1.2 R3.2 R1.3 R3.3 R2.1 R4.1 R2.2 R4.2 R2.3 R4.3

  20. Breadth-first reasoning R4.3 R4.2 R4.1 R1.2 Let say engine DOES turn over. R2.1 and R3.1 fails. Place Rule 4 in the memory. R1.1 is not askable. Ask R2.1. Ask R4.1 and R4.2. Let say the answers are affirmative. R4.3 is therefore true. Indeed, the engine is getting gas. First pass of the 4 rules is done.

  21. Computer, explain yourself: why and how • In the animal guessing game, the ES asks you, the user, questions. • Computer, why are you asking me this question? What are you trying to do? • The answer is simply the current rule that it is attempting to fire. • Computer, how do you arrive at this conclusion? • The answer is a trace of fired rules that led to this conclusion.

  22. Computer, explain yourself This dialogue begins with the computer asking the user about the goals present in working memory. Gas in fuel tank? Yes Gas in carburetor? Yes Engine will turn over? Why It has been established that the engine is getting gas. Current rule is Rule 1: If 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 Engine will turn over? Yes How come the problem is spark plugs?

  23. Control schemes Backward chaining Goal-driven E.g., Prolog starts with a goal to prove, and looks for rules that will help in proving it. More focused, purposeful. Forward chaining Data-driven Starts with the facts, and sees what rules apply, what can be true given the facts.

  24. MYCIN dialog • MYCIN: Has the patient had neurosurgery?USER: No.MYCIN: Is the patient a burn patient?USER: No.… • MYCIN: It could be Diplococcus..

  25. MYCIN • Many antibiotics have the suffix "-mycin". • Helped physicians diagnose bacterial infections. • Rule-based system • Backward chaining • I guess it is Diplococcus • Let see if I am right • Start with set of hypotheses on possible diseases - try to prove each one, asking additional questions of user when fact is unknown.

  26. A MYCIN rule • MYCIN’s knowledge base consisted of set of IF-THEN rules, e.g., IF the infection is primary-bacteremia AND the site of the culture is one of the sterile sites AND the suspected portal of entry is the gastrointestinal tract THEN there is suggestive evidence (0.7) that infection is bacteroid.

  27. Certainty factors • MYCIN uses certainty factors that allowed an assessment of the likelihood, if no one bacteria was certain. • For each possible bacteria: • Using backward chaining, try to prove that it is the case, finding the certainty factor • Find a treatment which covers all the bacteria above some level of certainty.

  28. Medical expert systems today • Medical expert systems were quite effective in evaluations comparing their performance with human experts. • The successful ones: • Support the physicians decisions, rather than doing the whole diagnosis. • Include many useful support materials, such as report generating tools, reference material etc.

  29. Experts, call center, users • Your company provides a system (a product like, online stock trading, online poker) run by users on the Web. • Your human experts maintain the system and troubleshoot the logic. • When they identify a new problem, they enter the rules required to diagnose the problem. • This diagnostic knowledge is used by a call center to help users running into problems when they are using your system. • The call center support staff gets the benefit of the improved diagnostics instantly. There is no need to retrain your support group. • No need for users to directly access the costly experts.

  30. ES today: Knowledge automation via the Web • Web-based software, software as a service, or cloud computing. • E.g., Vanguard System provides a web-based service to help its customers to develop different ESs according to each customer's needs. • Customers include insurance claims company, retirement planning company, computer network security company, etc.

  31. Conclusion • Knowledge engineers are concerned with • the representation chosen for the expert's knowledge declarations • the inference engine used to process that knowledge. • Effective systems have been developed that capture expert knowledge in many problem domains. • Rule-based systems provide way of reasoning on knowledge based on Condition-Action rules. • Two main ways to perform reasoning: forward or backward chaining. • Forward: start with facts • Backward: start with hypotheses • Experts can create ESs directly by using an ES shell or through a web-based ES development service.

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