CCSB354 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.

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 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!) the system can operate with only a few rules (as the first prototype) execution is done by using heuristic and logic 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 changes in programs are tedious

Comparison (II) Conventional Systems do not usually explain why or how conclusions were drawn need complete information to operate Efficiency is a major goal easily deal with quantitative data Expert Systems explanation is part of most ES can operate with incomplete or uncertain information Effective is the major goal easily deal with quantitative data

RIGHT TASKS FOR RIGHT SYSTEMS Facts that are known Expertise available but is expensive Those involved Analyzing large/diverse data E.g. Production scheduling & planning, diagnosing and troubleshooting, etc.

Generic Categories of Expert Systems (1) Diagnosis inferring system malfunctions from observations Interpretation inferring situation descriptions from observation Prediction inferring likely consequences of given situations

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 Debugging, Monitoring & 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 superficial Rapid degradation of performance Interfaces are crude (not impressive) No GUI Inability to adapt to more than one type of reasoning (in most cases)

TYPICAL 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)

Key components of an Expert Systems Another view: Key components of an Expert Systems

Explanation Facility Why need it? It provides sound reasoning besides quality result. Common types question seeking for an explanation “How” a conclusion was reached? “Why” a particular question was asked?

Importance of Explanation It can influence the ultimate acceptance of an Expert System. It can serve as a debugging tool. It is used as a tutoring system for new knowledge engineer in a project team. Who needs explanation? our clients: in order to convince them to buy. the knowledge engineer: to check if all specifications are met?

Approaches Used (1) Canned Text Problem: Prepared in advance all questions and answers as text strings When a question is asked, the system will find explanation module and displays the corresponding answer Problem: Difficult to secure consistency when the KB size grows bigger It is only suitable for slow changing system

Approaches Used (2) Paraphrase Tree Traversing is used For examples, to answer “why..’ and ‘how..’ types of question The explanation module will look up a search tree to answer “WHY this happened..” The explanation module will look down the tree to see sub goals that were satisfied to achieve the goal state

Most expert systems are rule-based. Rule-based Systems Recall that: Most expert systems are rule-based.

FACTS AND RULES (revisited) 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

Reasoning (Chaining) Systems Forward chaining it is used to predict the outcome from various facts Facts are conditions in a rule It is also used to find a suitable goal that all factors can be satisfied Backward chaining it is useful when trying to determine the reason once something has occurred It is used when the result for a problem is already found, and we want to know how

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?

For your information… supplementary topic

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 !

Example Select Auto is an expert system designed to assist a user to make a right decision of buying a new car. It will review prospective cars that match with users’ need and preference

The car is made in Quality is 1. the United State 2. foreign countries 3. Don’t know 2 Quality is 1. the highest concern 2. of high concern 3. of moderate concern 4. Don’t know 1

Price of the car is RULE NUMBER: 5 1. important 2. unimportant 3. don’t know WHY RULE NUMBER: 5 IF Price of a car is important and (2) The payment is in installments THEN The monthly payment is determined

Price of the car is 1. important 2. unimportant 3. don’t know The monthly payment is no more than 1. $100 2. $150 3. $200 4. $250 4

The most considered factor in making a decision to buy a car is 1. Price 2. Fuel economy 3. quality 1, 3

Result Values based on -100 to +100 system VALUE Toyota Corolla 51 Proton Perdana 43