1. EXPERT SYSTEMS Part I

2. What is there in an expert system?
Components of an expert system
Knowledge acquisition and knowledge engineering
Explanations
User interface
Expert system shells
Expert systems demos

3. Introduction
Expert systems solve real problems which normally would require a specialized human expert (such as a doctor or a lawyer).
Knowledge characteristics:
heuristic in nature,
based on ``rules of thumb''

4. Domains
Medicine
Engineering
Geology
Computer Science
Business
Law
etc

5. Types of problems
diagnosis (e.g., of a system fault, disease or student error)
design (of a computer systems, hotel etc)
interpretation (of, for example, geological data

6. Rule Based Production Systems
Most ES are rule-based production systems
Their knowledge is represented in the form of production rules to provide diagnoses or advice on the basis of input data.
Production rules are if-then rules
IF it rains, then you will get wet
IF it rains, then you should wear a coat

7. Components of an expert system
User
Inter-
face
Explanation
system
Inference engine
Knowledge base editor
Case specific data: Working storage
Knowledge base
Expert system shell

8. Expert system knowledge base
Domain inference rules and object descriptions
Case specific data
Knowledge base editor: creating and updating the knowledge base

9. Knowledge acquisition and knowledge engineering
Domain expert
Knowledge engineer
Knowledge acquisition : The process of "extracting" the knowledge from the domain expert and representing the knowledge in a suitable form that can be used by the expert system

11. Inference Engine
Forward chaining: derive all conclusions given certain facts
Select applicable rules
Choose a rule (conflict resolution)
Execute the rule and update the case-specific data
Backward chaining: prove a given goal by verifying the conditions in the IF-THEN rules
Coping with uncertainty
Mixed reasoning

12. Forward Chaining
Forward chaining is a reasoning model that works from a set of facts and rules towards a set of conclusions, diagnoses or recommendations.
When a fact matches the antecedent of a rule, the rule fires, and the conclusion of the rule is added to the database of facts.

13. Conflict Resolution More than one rule will may be used
A conflict resolution strategy decides which rule to use.
Strategies:
give rules priorities and use the conclusion that has the highest priority.
apply the rule with the longest antecedent
apply the rule whose condition was most recently added to the database.

14. Rete Algorithm for Forward Chaining
Reorganize the rules in a network so that the conditions are linked to the rules, i.e. rules are indexed by their antecedents
When a new conclusion is made or a new fact arrives, examine only those rules whose antecedents contain the new conclusions or facts
The algorithm makes forward chaining much faster at the expense of greater memory requirements

15. Backward Chaining Used when a particular conclusion is to be proved.
Works back from a conclusion towards the original facts.
When a conclusion matches the conclusion of a rule in the database, the system tries to prove the antecedents of the rule.

16. Coping with uncertainty
The ability of the system to reason with rules and data which are not precisely known.
Certainty factors:
Apply to the known facts: how much we trust the recorded facts
Apply to the rules: how much we trust the rules

17. Meta Rules Meta rules : knowledge about how the system works.
Example of meta rules:
The rules that determine the conflict resolution strategy
Meta rules might define that knowledge from Expert A is to be trusted more than knowledge from Expert B.

18. Meta Rules Implementation of meta rules:
Implicit – in the inference engine (e.g. conflict resolution)
Explicit - treated by the system like normal rules, but are given higher priority

19. Mixed Reasoning
Collect initial data and use forward reasoning to determine a set of possible conclusions
Use backward reasoning to prove or disprove each conclusion

20. Example of Mixed Reasoning in Medical ES
Collect data to build hypotheses
For each hypothesis H:
If H is in the facts database, it is proved.
Otherwise, if H can be determined by asking a question, then enter the user’s answer in the facts database. Hence, it can be determined whether H is true or false.
Otherwise, find a rule whose conclusion is H. Now apply this algorithm to try to prove this rule’s antecedents.
If none of the above applies, we have failed to prove H.

21. Explanations Why a particular question is asked
How a conclusion has been reached
Why not some other conclusion
What if a particular symptom is present
Terminology – explain the terms used by the system

22. User interface End-users Knowledge engineer Experts Requirements:
Self-consistent
Consistent with widely used interface systems
Provide navigation help
Incorporate user model

23. Expert System Shells The shell is a piece of software which contains
the user interface,
a format for declarative knowledge in the knowledge base, and
an inference engine.
The knowledge engineer uses the shell to build a system for a particular problem domain.

24. CLIPS CLIPS is C Language Integrated Production System –
an expert system shell.
CLIPS uses a
LISP-like notation
to enter rules.

25. CYC A frame based production system.
Uses a database of over 1,000,000 facts and rules, encompassing all fields of human knowledge.
CYC can answer questions about all kinds of knowledge in its database, and can even understand analogies, and other complex relations.