Revision
Chapter 1
Introduction to Expert Systems
1- An expert system is a computer system that acts in all respects, with the decision-making capabilities of a human expert.
2- Expert System Main Components are Knowledge base and Inference engine.
3- An expert’s knowledge is specific to one problem domain.
4- The expert’s knowledge about solving specific problems is called the knowledge domain.
5- From the advantages of Expert Systems are :
• Increased availability
• Reduced cost
• Reduced danger
• Performance
• Multiple expertise
6- The knowledge of an expert system can be represented in a number of ways, including IF-THEN rules.
7- The process of building an expert system:
• Eliciting knowledge by the knowledge engineer
• Coding the knowledge by the knowledge engineer
• Evaluation by the expert
8- The algorithm is an ideal solution guaranteed to yield a solution in a finite amount of time.
9- When an algorithm is not available or is insufficient, we rely on artificial intelligence (AI).
10- Shallow knowledge is based on empirical and heuristic knowledge.
11- Deep knowledge is based on basic structure, function, and behavior of objects.
1
12- Elements of an Expert System are :
• User interface
• Exploration facility.
• Working memory.
• Inference engine.
• Agenda.
• Knowledge acquisition facility.
• Knowledge Base.
13- Production rules can be expressed in IF-THEN pseudo code format.
14- In rule-based systems, the inference engine determines which rule antecedents are satisfied by the facts.
15- the inference engine determines the executions of the rules by the following cycle :-
conflict resolution
execution ( act)
match
16 - General Methods of Inference are :-
Forward chaining (data-driven)
Backward chaining (query/Goal driven)
17- Forward chaining - reasoning from facts to the conclusions resulting from those facts.
18- Backward chaining- reasoning in reverse from a hypothesis, a potential conclusion to be proved to the facts that support the hypothesis.
2
Chapter 2
Reasoning Under Uncertainty
1- Uncertainty is essentially lack of information to formulate a decision.
2- Deductive reasoning – deals with exact facts and exact conclusions 3- Inductive reasoning – premises support the conclusion but do not
guarantee it.
4- Errors Related to Hypothesis are :-
Type I Error – accepting a hypothesis when it is not true – False Positive.
Type II Error – Rejecting a hypothesis (theory) when it is true – False Negative
5- Errors Related to Measurement are:-
Errors of precision
Errors of accuracy
Unreliability
Random fluctuations
Systematic errors result from bias
6- Where deduction proceeds from general to specific, induction proceeds from specific to general.
7- When rules are based on heuristics, there will be uncertainty.
8- Experimental probability defines the probability of an event, as the limit of a frequency distribution.
3
9- Subjective probability deals with events that are not reproducible and have no historical basis on which to extrapolate.
10- Compound probabilities can be expressed by:
11- Independent events are events that do not affect each other. For pair wise independent events,
12- The probability of an event A occurring, given that event B has already occurred is called conditional probability.
13- Temporal Reasoning is reasoning about events that depend on time.
14- Transition matrix – represents the probabilities that the system in one state will move to another.
15- State matrix – depicts the probabilities that the system is in any certain state.
4
