1. EXPERT SYSTEM 1 Dr. Abbas Fadhil M. A. AL-Juboori Computer Science Dept. – Kerbala University abbaszain2003@yahoo.com Abbas.aljuboori@uokerbala.edu.iq

2. From the Father of Expert System: An intelligent program that uses knowledge and reasoning procedure to solve problems that require significant human expertise for their solutions. - Edward Feigenbaum

3. A system that uses human K captured in a computer to solve problems that ordinarily require human expertise Turban & Aronson (2001) A computer program that represents and reasons with K of some specialist subject with a view to solving problem or giving advice Jackson (1999) A computer program designed to model the problem-solving ability of a human expert Durkin (1994) A computer program that emulates the reasoning of human experts in a problem domain Awad (1996) From others:

4. As a field,  It is a branch of AI As a technology,  It is the most widely applied AI technology  Among the first to be commercialized As an application,  It is a computer program  It ‘transfers’ (i.e. it acquires and represents) practical knowledge (i.e. expertise/rules of thumb/heuristic) from human expert to computer

5.  It reasons (or it ‘thinks’) with what it ‘transfers’  It can either  support decision makers (by recommending decisions) or  ‘replace’ them (by making decisions on behalf of experts, releasing them from routine tasks).

6.  After all, An is one who possesses specialized skill, experience, and knowledge that most people do not have along with the ability to apply this knowledge using tricks, shortcuts, and rules-of-thumb to resolve a problem efficiently. Harmon & King (1985) ES replicates a human expert. expert

7. Therefore, ES possess expertise  is  an extensive, task-specific knowledge held by experts  hard to capture. Capturing it is a major issue in ES development, and became a major concern of Knowledge Acquisition researchers.  With the expertise stored in its knowledge base, ES can provide expertise-based solutions which are imaginative, accurate and efficient. Expertise

8.  Expert personnel is a valuable asset for any organization (as they are good at solving organizational problems, planning etc.) … yet they are  Perishable and irreplaceable  Geographically static  Not available 24/7  Emotionally affected  feel fear, stress etc … like other human beings.  Costly  to train and to consult

9. So, we have good reasons to build an ES!! 3 main reasons:  To replace human expert  To assist human expert  To gain competitive advantage

10.  To replace  to eliminate  Reasons for replacing experts  To preserve their expertise  To disseminate their expertise in less expensive manner  To make expertise available after hours  To make expertise available at several locations  To free experts from routine thus they can focus on the other critical tasks  To avoid experts from danger

11.  ES as an aided tool to  improve human expert’s productivity  maintain consistency in their decisions  deal with the complexity of the tasks  make available the information that is difficult for experts to recall

12.  Due to the benefits this technology can offer  Exemplar:  Digital Equipment Corporation ▪ R1/XCON  American Express ▪ Authorizer’s Assistant  Coopers & Lybrand ▪ ExperTax

13. Knowledge base Working memory Inference engine User Interface

14. Long-term memory SENSORSENSOR Short-term memory Brain The components actually mimic what is in humans. = Knowledge base = interface = Inference engine = Working memory Environment = People/sensor etc that provide input to our brain

15.  Knowledge base  “contains the domain knowledge”  Facts  Heuristics or rules that direct use of knowledge to solve specific problems in a particular domain.  Typical representation:  Rules (IF x AND y THEN z @ x  y  z)  Example (for predicting weather): ▪ IF cloudy = yes AND temperature = low AND humidity = high THEN it will rain. ▪ IF cloudy = no AND temperature = high AND humidity = low THEN it will sunny

16.  A storage area for current data  i.e. facts entered by user during consultation with ES (e.g. symptoms of a disease)  Input data can also be loaded from external storage such as databases, spreadsheets or sensors.  Also a place where intermediate conclusions or the new facts inferred by ES are stored  Non-permanent – content will be deleted when the session ends.

17.  Known as rule interpreter in rule-based ES.  Is modelled after human expert’s reasoning.  Typically, inference engine utilized 2 control strategies:  Backward Chaining (goal driven) ▪ determine fact in the conclusion to prove the conclusion is true.  Forward Chaining (data driven) ▪ premise clause match situation then assert conclusion.

18.  Facilitates all communication between user and ES.  Communication are in natural language style, interactive and follow closely the conversation between humans.  Two types of interaction:  ES ask for information through questions, provide the results and display the explanation.  User supply answers, receive the results or query the system (i.e. getting explanation)

19.  Two types of explanation:  WHY ▪Explain why the system asked the question.  HOW ▪Explain how ES arrived at the conclusion.  Justify the validity of the system’s findings  increase user confidence and trust

20. EXPERT PROJECT MANAGER K SYSTEM DEVELOPER ENGINEER/ ANALYST K MANAGER USER KS manages uses designs & implements validates elicits knowledge from elicits requirements from delivers analysis models to defines K strategy initiates K development projects facilitates K distribution Source: Schreiber et al. (2000)

21.  Knowledge engineering  Methodology for building an ES  6 phases of knowledge engineering:  Problem assessment  Knowledge acquisition  Design  Testing  Documentation  Maintenance

23.  Choice of tools and approaches for developing ES includes:  Programming languages  Support aids and tools  Ready-to-use customized packages for industry and government  ES shells  Which tools to adopt depends on:  The nature of the problem  The skill of the builder  The function ES is expected to perform (either diagnoses or monitoring)

24. Domain expert Provide knowledge or method to solve problem Knowledge Engineer Gain knowledge from expert Transfer/represent knowledge into a computer User Can be the end user or expert himself

25.  ES has been applied to perform/solve the following task/problem  Control – meeting certain standards/specifications  Design – configuring objects under specific constraints  Diagnosis – inferring malfunction/disease and recommend solutions/treatment  Planning – designing actions  Monitoring – comparing observation to expectation  Selection – identifying the best choice(s) from a list of actions

26.  ES has been applied to perform/solve the following task/problem  Interpretation – infer situation description from observation  Prediction – infer likely consequences of the given situation  Debugging – prescribe remedies for malfunction  Repair – execute a plan to administer a prescribed remedy  Instruction – diagnose, debug and correct student’s misconception

27. BENEFITSLIMITATIONS Reduce decision making timeWork well only within a narrow domain of knowledge Improve production operationsCan make mistakes Increase output and productivityRisk of knowledge quickly become obsolete Can be used as tools for staff trainingOngoing reliance on experts Retention of scarce expertiseKnowledge is not always available Upgrade performanceDifficult to extract expertise from human experts Relatively affordable expertiseUser lack of trust can impede use Improve quality of products/services

28.  In general, ES works by matching the facts with its knowledge base content, and display the output to user Knowledge base Working memory Inference engine User Interface A R12: A  B Modus Ponens! B

29.  In detail, it depends on what control strategy each ES’s inference engine utilizes, either forward chaining or backward chaining or both  The principle of chaining is governed by modus ponens. A  B  C A B C  Chaining signifies linking of a set of pertinent rules.

30.  Goal rule  A rule in which its conclusion is not a premise of any other rules in the knowledge base  E.g. R1: (A  B)  C  D R2: D  G  T R3: P  Q  B  R2 is the goal rule as its conclusion, T, is NOT one of the premises of the other rule (i.e. R1 and R3)

31.  Sub-goal rule  A rule in which its conclusion is also a premise of the other rules or in the goal rule.  E.g. R1: (A  B)  C  D R2: D  G  T R3: P  Q  B  R1 and R3 are sub-goal rule as their conclusions are premises of the other rules ▪ Conclusion of R1, i.e. D, is a premise of R2 ▪ Conclusion of R3, i.e. B, is a premise of R1

32.  Primitive premise  A premise that is not a conclusion of any other rules  E.g. R1: (A  B)  C  D R2: D  G  T R3: P  Q  B  A, C, G, P and Q are primitive premises. WHY? … Look at the THEN part of R1, R2 and R3 ▪ None of these rules has either A or C or G or P or Q as a conclusion.

33.  Non-primitive premise  A premise that is also a conclusion of the other rule(s)  E.g. R1: (A  B)  C  D R2: D  G  T R3: P  Q  B  B and D are non-primitive premises. WHY? … Look at the IF part of R1 and R3. ▪ R1 has D as a conclusion while R3 has B as a conclusion. ▪ Both B and D are premises, at the same time they also are conclusions, thus they are non-primitive premises.

34.  “Rule fire”  A “rule fire” means “rule is concluded”. In other words, it refers to a state where the conclusion of that rule is proved as true, because its premise(s) is true”  E.g. R1: (A  B)  C  D ▪ If A and B are true, or if C is true, then we say “R1 fire” with a conclusion D true.  “Rule not fire”  Is a vice versa of “rule fire” … due to its premise(s) not true.

35.  Backward chaining overview  An Inference strategy that attempts to prove a hypothesis by gathering supporting information  The system works from the goal by chaining rules together to reach a conclusion or achieve a goal  In other words, it start with the goal, and then looks for all relevant, supporting premises that lead to achieving the goal.

36. 1.Identify the goal. 2.Search for the goal rule. 3.If found, check its premise(s).  If premise is primitive, check if it is in the working memory, ask user a question if it is not there.  If premise is non-primitive, jump to the rule where it belongs to as a conclusion (sub-goal rule). Repeat Step 3. 4.Repeat Step 3 by jumping and firing all relevant sub-goal rules. 5.Finally, fire the goal rule.

37. With example: R1: (A and B) or C implies D R2: D and G implies T ….. goal rule 1. Identify the goal …. T. 2. Identify the goal rule ….. R2. 3. Check R2’s first premise, i.e. D ▪ D is non-primitive …. it belongs to R1 as conclusion, so jump to R1. ▪ Check R1’s premises.  A is primitive. If it is in working memory, continue checking B. If not, ASK user a question. If the answer is ‘yes’, continue checking B. If ‘no’, check C.

38. 4. Either A and B are true or C is true, fire R1. 5. Jump back to R2. 6. Repeat 3 with R2’s second premise, i.e. G. ▪ G is primitive. If it is in working memory, fire R2. Otherwise, R2 fail to fire and therefore, the goal cannot be proven. 7. End of step.

39.  Forward Chaining overview  An Inference strategy that begins with a set of known facts, derives new facts using rules which premises match the known facts, continues until goal reached or no more rules matches.  Begins with known data and works forward to see if any conclusions (new information) can be drawn.

40. 1. Get initial data and place it in working memory. 2.Scan the rules searching for matched premises. 3.If found  fire the rule  add its conclusion to working memory. 4.Repeat Steps 2 & 3 until no more match or goal is achieved.

41. With example: R1: (A and B) or C implies D R2: D or G implies T 1. Get initial data. 2. Scan the rules in sequence. ▪ If A and B are true, or C is true, R1 fires and D is inserted into working memory. D will cause R2 to fire when R2 is scanned. Process is then terminated as all rules have been scanned, and no more match can be done. ▪ If none of A, B and C true, continue scan the next rule, i.e. R2. If G is true, R2 fires and T is inserted into working memory. Process is then terminated as all rules have been scanned, and no more match can be done.

42. What if T is in R1? R1: (A and B) or T implies D R2: H or G implies T 1. Get initial data. 2. Scan the rules in sequence. ▪ If none of A, B and T is true, scanning is continued with R2. ▪ If H or G is true, R2 fires and T is inserted into working memory. End of 1 st cycle with conclusion T. ▪ The 2 nd cycle of scanning and firing rules begins. T is now in working memory, therefore R1 fires. D is concluded and inserted into working memory. End of 2 nd cycle with conclusions T and D. The most recent, i.e. D, becomes the final conclusion.

43.  Conflict resolution  A process to determine which rule to fire (when the contents of the WM can cause >1 rule to fire)  Resolution strategy: ▪ Establish the goal and stop the system when the goal is attained ▪ The order of the rules that conclude the goal is important (the engine will fire the first one located). ▪ Assign rules with the priority values (reflect rule preferences) ▪ The system scans the rules, determines the rules to fire, and fire the ones with highest priority.

44. AttributeBackward ChainingForward Chaining Also known asGoal-drivenData-driven Starts fromPossible conclusionNew data ProcessingEfficientSomewhat wasteful Aims forNecessary dataAny conclusion (s) ApproachConservative/cautiousOpportunistic Practical ifNumber of possible final answers is reasonable or a set of known alternatives is available Combinatorial explosion creates an infinite number of possible right answers Appropriate forDiagnostic applicationScheduling and monitoring Example of application Selecting a specific type of investment Making changes to corporate pension fund