CSNB234 ARTIFICIAL INTELLIGENCE Chapter 7 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.

An “expert system” is defined as “A computerized clone of a human expert” (Definition taken from Oxford Science Publication)

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 EXPERT: 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

Comparison (I) Expert Systems Conventional 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 (this is called fast prototyping) changes in the rules are easy to accomplish 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 (i.e. it is algorithmic)

Comparison (II) Expert Systems 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 heuristic based on rules of logic E___________ is the major goal easily deal with q______ data

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.

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 Other categories include: monitoring, debugging, control, instruction

BENEFITS OF EXPERT SYSTEMS (I) Expertise in a field is made available to many more people (even when human experts are not around in the company). Top experts’ knowledge gets saved rather than being lost, when they retire or should they have resigned. Facts are stored in a “Systematic” way. & Easy to keep on adding new knowledge on it Allows human experts to handle more complex problems rapidly and reliably.

Early EXPERT SYSTEMS (70s – mid 80s) MYCIN USES RULE-BASED SYSTEM GOAL-DRIVEN RULES INCORPORATED IN MYCIN REFLECTED UNCERTAINTY ASSOCIATED WITH KNOWLEDGE CERTAINTY FACTOR WAS USED TO DERIVE CONCLUSION DENTRAL WAS DEVELOPED IN STANFORD UNIVERSITY TO ANALYZE AND INTERPRET CHEMICALS AND THEIR MOLECULAR STRUCTURES DEVELOPERS INCLUDE JOSHUA LEDERBERG (NOBEL PROZE WINNER IN GENETICS) EXPERT’S “KNOW-HOW” ARE EXPRESSED IN RULES; RULE-OF-THUMB TECHNIQUE IS USED PROSPECTOR KBS TO INTERPRET GEOLOGIC DATA FOR MINERALS EXPLORATION INCOPORATED BAYES THEOREM (PROBABILISTIC REASONING APPROACH)

Early EXPERT SYSTEMS (70s – mid 80s) XCON RULE-BASED SYSTEM, DATA-DRIVEN REVEAL FUZZY LOGIC USED CENTAUR RULES AND FRAMES-BASED SYSTEM HEARSAY I – for speech recognition

Characteristics common to early ES Could perform at a level equivalent to human experts Large amount of domain specific knowledge Rule-based systems: knowledge incorporated in the form of production rule

Popular Expert Systems Application Domain Electronics : helps in VLSI/ULSI design Law : system serves as an auditor Manufacturing : in production & process controls Medicine : illness diagnosis Chemistry : synthesis planning

EXPERT SYSTEMS: LIMITATIONS SYSTEMS ARE TOO SUPERFICIAL RAPID DEGRADATION OF PERFORMANCE INTERFACES ARE STILL CRUDE INABILITY TO ADAPT TO MORE THAN ONE TYPE OF REASONING E.g. either forward or backward and not both

The Resistors Domain experts Non-experts Other information technologists such as DBA, Network specialists Users Management Troublemakers

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)

Figure: Key components of an Expert Systems

Explanation Facility Why need it? Common types 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 acceptance of an Expert System. Use as a debugging tool. Use as a component of a tutoring system. Who needs explanation? Our clients : to be convinced to purchase. Knowledge Engineer: to check if all specifications are 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

Most expert systems are rule-based. 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 (revision) A mammal is an animal A bird is an animal Arthur is a man Ben drives a car Catherine has blue eyes 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.

Chaining Systems 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

Inference Strategies (I) Conclusion (Goals) Input Data Few Items (For Example, User Specifications for a Computer System) Many Possibilities (For Example, a Computer Configuration) (a) Forward Chaining: IF - Part Matches Shown

Inference Strategies (II) Input Data Conclusion (Goals) Extensive; Much of the Data Obtained by the System Querying the User (For Example, Investor’s Profile) Few Possibilities (Known in Advance ((For Example, Investment Options) (b) Backward Chaining: THEN - Part Matches Shown

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 considered ‘too difficult’.

Nice to know…

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 !