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CCSB354 ARTIFICIAL INTELLIGENCE. Chapter 8 Introduction to Expert Systems. (Chapter 8, Textbook) (Chapter 3 & Chapter 6, Ref. #1). Instructor: Alicia Tang Y. C. EXPERT SYSTEM (ES). Definition
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CCSB354ARTIFICIAL INTELLIGENCE Chapter 8 Introduction to Expert Systems (Chapter 8, Textbook) (Chapter 3 & Chapter 6, Ref. #1) Instructor: Alicia Tang Y. C.
EXPERT SYSTEM (ES) • Definition • ES is a set of computer programs that can advise, consult, diagnose, explain, forecast, interpret, justify, learn, plan and many more tasks that require ‘intelligence’ to perform.
From Oxford Science Publication An expert system is defined as“a computerized clone of a human expert”
EXPERT SYSTEMS: CHARACTERISTICS • Perform at a level equivalent to that of a human expert. • Highly domain specific. • Adequate response time • Can explain its reasoning. • It can propagate uncertainties and provide alternate solutions through probabilistic reasoning or fuzzy rules .
AN EXPERT AND A SHELL • ES SHELL • A special purpose tool designed for certain types of applications in which user supply only the knowledge base (e.g. EMYCIN) • It isolates knowledge-bases from reasoning engine • Hence software portability can be improved • EXPERT: • An expert in a particular field is a person who possess considerable knowledge of his area of expertise Domain-specific
Shell Concept for Building Expert Systems KB e.g. rules Consultation Manager KB Editors & debugger shell Inference Engine Explanation Program KBMF
Conventional Systems information & its processing are combined in one sequential program programs do not make mistake (but programmers do make it) the system operates only when it is completed execution is done on a step-by-step basis ( ) Expert Systems knowledge base is separated from the processing (inference) mechanism program may make mistake (we want it to make mistake!) explanation is part of most ES the system can operate with only a few rules ( ) changes in the rules are easy to accomplish Comparison (I)
Conventional Systems changes in programs are tedious do not usually explain why or how conclusions were drawn need complete information to operate E__________ is a major goal easily deal with q_________ data Expert Systems can operate with incomplete or uncertain information execution is done by using h_________ and logic E___________ is the major goal easily deal with q______ data Comparison (II)
RIGHT TASKS FOR RIGHT SYSTEMS • Facts that are known • Expertise available but is expensive • Analyzing large/diverse data • E.g. Production scheduling & planning, diagnosing and troubleshooting, etc. (will see them later on)
Generic Categories of Expert Systems (1) • Interpretation • inferring situation descriptions from observation • Prediction • inferring likely consequences of given situations • Diagnosis • inferring system malfunctions from observations
Generic Categories of Expert Systems (2) • Design • configuring objects under constraints • Planning • developing plans to achieve goals • Repair • executing a plan to administer a prescribed remedy Others are: monitoring, debugging, control, instruction
BENEFITS OF EXPERT SYSTEMS (I) • Expertise in a field is made available to many more people (even when human expert is not present). • Top experts’ knowledge gets saved rather than being lost, when they retire • “Systematic”; no factors forgotten. • Easy to keep on adding new knowledge • Allows human experts to handle more complex problems rapidly and reliably.
EXAMPLES of EXPERT SYSTEMS • MYCIN • USES RULE-BASED SYSTEM, GOAL-DRIVEN • EMPLOYED CF TO DERIVE CONCLUSION • PROSPECTOR • INCOPORATED BAYES THEOREM (PROBABILITY) • Interpret geologic data for minerals • XCON • RULE-BASED SYSTEM, DATA-DRIVEN • REVEAL • FUZZY LOGIC USED • CENTAUR • RULES AND FRAMES-BASED SYSTEM • DENTRAL – interpret molecular structure • HEARSAY I – for speech recognition
LIMITATIONS • SYSTEMS ARE TOO SUPERFICIAL • RAPID DEGRADATION OF PERFORMANCE • INTERFACES ARE STILL CRUDE • INABILITY TO ADAPT TO MORE THAN ONE TYPE OF REASONING (in most cases)
STRUCTURE OF AN EXPERT SYSTEM Consultation Environment (Use) Development Environment (Knowledge Acquisition) User Expert Facts of the Case Recommendation, Explanation User Interface Explanation Facility Knowledge Engineer Inference Engine Facts of the Case Knowledge Acquisition Facility Working Memory Knowledge Base Domain Knowledge (Elements of Knowledge Base)
Explanation Facility • Why need it? • It provides sound reasoning besides quality result. • Common types • “How” a conclusion was reached • “Why” a particular question was asked
Importance of Explanation • It can influence the ultimate a________ of an Expert System. • Use as a d______________ tool. • Use as a component of a tutoring system. Who needs explanation? Clients : To be convinced. Knowledge Engineer: Specifications all met?
Approaches Used (1) • Canned Text • prepared in advance all questions and answers as text • system finds explanation module and displays the corresponding answer • problem: • difficult to secure consistency • suitable for slow changing system only
Approaches Used (2) • Paraphrase • Tree Traverse • to answer WHY • look up the tree • to answer HOW • look down the tree to see sub goals that were satisfied to achieve the goal
Rule-based Systems In expert system development, a tool is used to help us to make a task easier. The tool for machine thinking is the Inference Engine. Most expert systems are rule-based.
FACTS AND RULES • FACTS : • A mammal is an animal • A bird is an animal • Adam is a man • Ben drives a car • RULES : • If a person has RM1,000,000 then he is a millionaire. • If an animal builds a nest and lays eggs then the animal is a bird.
Examples of rules: Rule 1: if you work hard and smart then you will pass all examinations Rule 2: if the food is good then give tips to the waiter Rule 3: if a person has US1,000,000 then he is a millionaire
Forward Chaining and Backward Chaining These are methods for deducing conclusions. The former predicts the outcome (conclusion) from various factors (conditions) while the latter could be very useful in trying to determine the causes once something has occurred. Detailed description and working examples of rule-based systems and their reasoning methods will be dealt separately in other chapters.
Forward it predicts the outcome from various factors (conditions) Backward it could be very useful in trying to determine the cause (reason) once something has occurred Chaining Systems
Inference Strategies (I) Conclusion (Goals) Many Possibilities Input Data (a) Forward Chaining
Inference Strategies (II) Conclusion (Goals) Input Data Few Possibilities (b) Backward Chaining
Exercise #1 You have seen what tasks are “just right” for ES and now you are required to answer the following question: • List a “Too hard” task for computers and explain briefly why they are said too difficult. And, why?
RULE-BASED VALIDATION • There are essentially 5 types of inconsistency that may be identified, these are: • Redundant rules • Conflicting rule • Subsumed • Unnecessary Premise(IF) Clauses • Circular rules
REDUNDANT RULES • Rule 1 • IFA = X AND B= Y THEN C = Z • Rule 2 • IF B=Y AND A=X THEN C=Z AND D=W • Rule 1 is made redundant by rule 2.
CONFLICTING RULES • Rule 1 • IF A = X AND B= Y THEN C = Z • Rule 2 • IF A=X AND B=Y THEN C=W • Rule 1 is subsumed by rule 2 thus becomes unnecessary.
SUBSUMED RULES • Rule 1 • if A = X AND B= Y THEN C = Z • Rule 2 • if A=X THEN C=Z • to be revised.
UNNECESSARY PREMISE (IF) CLAUSES • Rule 1 • IF A = X AND B= Y THEN C = Z • Rule 2 • IF A=X AND NOT B=Y THEN C=Z • Remove B=Y and NOT B=Y to have just one rule.
CIRCULAR RULES • Rule 1 • IF A = X THEN B = Y • Rule 2 • IF B=Y AND C=Z THEN DECISION=YES • Rule 3 • IF DECISION=YES THEN A = X • Restructure these rules !