Download Contingency Model - Artificial Intelligence - Lecture Slides and more Slides Artificial Intelligence in PDF only on Docsity!
The IF THEN contingency model
In the early 1970 Ed Feigenbaum at Stanford began working with
a simple model to codify knowledge, a model so simple that
people were initially skeptical about the results however they
were so successful that the first true AI application in industry
took place.
Expert Systems made AI known outside of the academic field.
Basically the idea behind the model is to represent the relation
between two pieces of data as an Implication
IF Antecedent THEN Consequent
A = > C (here the concept of implication is the Key)
The IF THEN equivalents
IF Antecedent THEN Consequent
A = > C
Consequent
LHS = Antecedent
RHS = Consequent
LHS
Imply
Equality
A C A = C A => C
0 0 1 1
0 1 0 1
1 0 0 0
1 1 1 1
Bayes A p(C) p(~C) TRUE 0.8 0. FALSE 0.3 0.
WITH P
STM C 1 2 3 4 A 1 0 0.2 0.8 0 2 0.5 0.3 0.1 0. 3 0 0 0.9 0. 4 0 1 0 0
Learning
0 0 1 0 1 0 0 0 0 0 1 0 0 1 0 0
Expert Systems Components
• User interface
• Knowledge acquisition module Knowledge base
• Inference engine
– control strategy
• Explanation facility
• Diverse Interfaces
Expert System Architecture
Data Base General data (facts) is stored here
DB
KB
Knowledge Base IF THEN rules are stored here
Queries A good I/O interface with the user
Conditions
Initial data conditions (facts)
Knowledge
Aquisition
New rules IF/THEN
Results Diagnosis,synthesis Why? & How?
Interface Syntactical and semantical processing
Inference Search KD and DB to solve a problem backward and forward tracking
Administration Login users and journaling, metadata
DBA
DB
KB
DBA
Data Base Updating
Data Base
- The DB is updated with the
data of facts and conditions for
a given situation
Data, conditions & facts
Knowledge Acquisition
From Buchanan et al. (1983):
“ the transfer and transformation of potential
problem solving expertise from some
knowledge source to a program ”
Backward chaining (DIAGNOSIS)
- Begins with a proposed conclusion
- Tries to match it with the “ then ” clauses of
rules
- Then looks at the corresponding “ if ” clauses
- Tries to match those with assertions, or with
the “ then ” clauses of other rules
Source:www.csc.uvic.ca/~csc212/lec05/chapter14.ppt 10
Forward chaining
SYNTHESIS
- Begins with assertions and tries to match
those assertions to “ if ” clauses of rules,
thereby generating new assertions
DB
KB
DBA
Why and How questions
Why? & How?
Knowledge Base
Inference
Results Data Base
tree traversal (forward)
- To answer HOW:
- It presents the performed tree
traversal so far executed (backward)
Rule exploring: Node Levels
Diagnosis
Possible explanation
Synthesis
Possible construction
2
1
3
4
5
6
7 RHS = Consequents = Conclusions
LHS = Antecedents = Premises
Search Strategies
in the KB
Rule # Antecedents Consequents Traversal Number of Ref
Prob # Prob # Prob # Prob # Prob # Prob Nodo Area Complex Use Ant's Con's
1 2
Synonyms Antecedent Consequent Type # Description Rules
- By Knowledge area
- By Node Level
- By Synonym
- By Complexity
- By Number of Antecedents
- By Use
LJournal
Reference
Trend Setters
• DENDRAL
• MYCIN with EMYCIN & TEIRESIAS
• INTERNIST
• PROSPECTOR
• R1/XCON
Trend Setters
• MYCIN (Ted Shortliffe)
– Knew about blood infectionsﾐ
– In one study, its recommendations were judged
preferrable or equal to those of five experts.
• INTERNIST (Harry Pople)
– Broader medical expertise
– In one study, it got 25 out of 43 diagnoses correct,
compared to 28 for clinical physicians and 35 for
experts.
Trend Setters
• MYCIN (Ted Shortliffe)
– Knew about blood infections
– In one study, its recommendations were judged preferable or
equal to those of five experts.
• Sample Rule:
– IF:
• (1) the stain of the organism is gram-positive,
• AND (2) the morphology of the organism is coccus,
• AND (3) the growth confirmation of the organism is clumps,
– THEN:
• there is suggestive evidence (0.7) that the identity of the
organism is staphylococcus.
