An Overview of Expert Systems

1. An Overview of Expert Systems

2. TOPICS • The nature of expertise • Who is an Expert, and Why? • The Characteristics of an Expert Systems • What Makes it different and Why ? • Additional Issues in Expert Systems • Knowledge acquisition (Building knowledge bases) • Knowledge assessment • Explanation facilities

3. Analogous to our previous ‘Forest vs. Tree’ Idea The Nature of Expertise • Assumes a highly specialized set of Skills • NOT just general knowledge • Assumes a very specialized problem domain • Assumes logic, problem solving and experience • NOT simple intuition or indefinable behaviors

4. Performance Expertise The Nature of Expertise • Who is an Expert?? • That is NOT an easy Question • There are many practitioner but very few experts • Notice that just because you have experience, that does NOT mean that you are an expert • Characteristics of Experts • Fast, ACCURATE, problem Solving • Pattern Recognition • Use of Heuristics – Based on past experience • Scarcity

5. The Nature of Expertise • Necessary Expert Traits • Be Recognized as an Expert • Know how they perform the task • Can NOT just act intuitively without being able to explain their behaviors • Have the time and ability to explain how they perform • Be Motivated to Cooperate

6. The Nature of Expertise • How do you know who is an expert?? • Also NOT an easy Question, although some are obvious • There are references, However (a few off the Internet): • ExpertPages.com: A directory for legal professionals in search of experts, expert witnesses, or consultants. Search by state, country, or subject area. http://www.expertpages.com/ • Experts Directory A searchable directory of experts from the legal, medical, journalism and other professions. http://www.experts.com Are they really Experts ??? Don’t Mortgage the House!

7. A computer program demonstrates artificial intelligence if it can “pass’ as a human (c. 1950) • In 1990, the Cambridge Center for Behavioral Studies began offering the $100,000 Loebner Prize to the first program whose responses were indistinguishable from a human’s 1912-54 Expert System Characteristics “An expert system is a computer program that represents and reasons with knowledge of some specialist subject with a view to solving problems or giving advice.” Jackson (1999) • Turing Test (No one has ever won)

8. Expert System Characteristics • Gary Kasparov vs. IBM’s Deep Blue • May 11, 1997 • Garry Kasparov resigned 19 moves into Game 6 • Deep Blue wins the Best of Six game series 3.5 to 2.5 • IBM Development Team wins $700,000 • Kasparov wins $400,000 • The first win by a computer program over an International Grand Master since man/computer games were first began in 1970

9. Expert System Characteristics • Basic Requirements • simulates human reasoning • Rule/Heuristic Based: Rule: If there is a potato in the tailpipe, the car will not start.Finding: There is a potato in the tailpipe.Conclusion: The car will not start. (Truth preserving inference) Rule:If there is a potato in the tailpipe, the car will not start.Finding:My car will not start.Conclusion:Therefore, there is a potato in the tailpipe. (Non-Truth preserving inference)

10. Expert System Characteristics • Basic Requirements • simulates human reasoning • Inference Engines • The ‘Driving’ Force in an Expert System • Reasons with any rule constructed via rule set manager • Searches for applicable rules • Evaluates the predicates of those rules to determine their “truth” • Executes the actions specified in “fired” (activated) rules

11. Expert System Characteristics • Basic Requirements • simulates human reasoning • Inference Engines • Forward Chaining • Corresponds to the idea of Deductive reasoning Theory Birds can Fly Hypothesis Ostriches Can Fly Observation (I Fly to Australia) OK – I was wrong ! Rejection Confirmation

12. Expert System Characteristics • Basic Requirements • simulates human reasoning • Inference Engines • Forward Chaining • Corresponds to the idea of Deductive reasoning • Consists of a condition part and an action part • Conditions (rules) are matched against the database • If true, the action is fired • The forward chaining engine cycles repeatedly until it runs out of rules or a rule instructs it to stop.

13. Expert System Characteristics • Basic Requirements • simulates human reasoning • Inference Engines • Forward Chaining • Backward Chaining • Corresponds to the idea of Inductive reasoning Theory Not all Birds can Fly Ostriches Can’t Fly (what a Moron I was!) Tentative Hypothesis Birds Flying, but no Ostriches Pattern Observation I’m back in The Australian Outback – Bird watching

14. Expert System Characteristics • Basic Requirements • simulates human reasoning • Inference Engines • Forward Chaining • Backward Chaining • Corresponds to the idea of Inductive reasoning • Involves trying to prove a given goal by using rules to generate sub-goals and recursively trying to satisfy them. • The engine looks at conclusions and determines all rules that could reach that conclusion • Each rule is then examined for its premises • If true, the rule is fired and a value is established • The process continues until all possible solutions are generated

15. Expert System Characteristics • Basic Requirements • simulates human reasoning • Knowledge Representation • Knowledge Bases • A repository (Database) of data and metadata • Contains all the Rules established by the manager • The data are stored as objects, which can be fired as needed • Includes Symbolic data • Includes Relationships between data • May be used in conjunction with a standard database

16. Expert System Characteristics • Basic Requirements • simulates human reasoning • Knowledge Representation • Deal with realistically complex Problems • Reach Multiple Conclusions • Especially as a result of backward chaining • Explain the conclusions reached • The logic used must be demonstratable • Deal with Missing Information • “Fuzzy Logic” • Non-numerical Analysis • Demonstrate High Performance • Should approximate the performance of the expert

17. ES Shell Inference Engine User Interface Knowledge Base Database Expert System Characteristics • Basic Requirements • ES Components A rule engine and scripting Environment

18. Expert Systems Decision Support Systems Expert System Characteristics • Basic Requirements • ES Components • Differences Between ES and DSS • Based On Expert • No Experts Available • Based on Logical Reasoning • Based on Numerical Analysis • System Questions User • User Questions System • Used Frequently • Used for Ad-hoc Problems • Final Solution(s) Provided • Outputs provided based Analysis • Very Accurate • Unknown Accuracy • Multiple Solutions • Always the same output • Learning Possible • Always the same output

19. AdditionalTopics • Knowledge Acquisition “The transfer and transformation of potential problem-solving expertise from some knowledge source to a program” - Buchanan et al. (1983) • Transfer of the Expert’s Knowledge as a set of rules into the Knowledge Base • Since the Expert is not expected to code the rules, a Knowledge Engineer is required • lengthy & intense interviews Required • slow (2 to 5 units of knowledge /day) ??? Why ??? • Imprecise, illogical, jargon or colloquialisms, experience, contextual detail, reliability of sources, ...

20. Additional Topics • Knowledge Acquisition • Example: How to find a forgotten Password: Expert (Computer Center Guru): Well, if it’s a YP pass-word, I first log on as root on the YP master KE: (Knowledge Engineer): Er, what’s the YP master? Expert: It’s the diskful machine that contains a database of network information KE: ‘Diskful’ meaning - ? Expert: -it has the OS installed on local disk KE: Ah. (scribbles furiously) So you log on… Expert: As root. Then I edit the password datafile, remove the encrypted entry, and make the new password map... This is the weakest link in the process !!

21. Me Neither I don’t get it ! AdditionalTopics • Knowledge Acquisition • Potential Solutions/Problems • automated knowledge elicitation • interactive programs/automated conversation • Problem: There are no Good Programs available (yet) • textual scanning • Parsing of conversations to extract the important components • Problem: NLP is still in its infancy • machine learning • deriving decision rules from examples • evaluating / weighting rules • performance optimization of rules • Problem: Only Limited Success to date

22. Additional Topics • Knowledge Acquisition • Knowledge Assessment • logical adequacy • sound & complete inferencing • heuristic Power • efficiency Vs. optimality (Effectiveness) • notational Convenience • How accurately do the rules reflect the logic?

23. AdditionalTopics • Knowledge Acquisition • Knowledge Assessment • Explanation Facility • Necessary to check validity of Solutions • The Chain of reasoning must be logged • Solution Accountability must be determined • Deficiencies must be corrected

24. AdditionalTopics • Knowledge Acquisition • Knowledge Assessment • Explanation Facility • Available Packages/Tools • Symbolic Manipulation Languages • LISP (LISt Processor) • Prolog • Expert Shells • CLIPS (Free Download:http://www.ghg.net/clips/CLIPS.html) • Jess (Free Download:http://herzberg.ca.sandia.gov/jess/) • Others: A good list can be found at • http://www-2.cs.cmu.edu/afs/cs/project/ai-repository/ai/areas/expert/systems/0.html

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