1. EXPERT SYSTEMS AND KNOWLEDGE REPRESENTATION Ivan Bratko Faculty of Computer and Info. Sc. University of Ljubljana

2. EPERT SYSTEMS
An expert system behaves like an expert in some narrow area of expertise
Typical examples:
Diagnosis in an area of medicine
Adjusting between economy class and business class seats for future flights
Diagnosing paper problems in rotary printing
Very fashionable area of AI in period

3. SOME FAMOUS EXPERT SYSTEMS
MYCIN (Shortliffe, Feigenbaum, late seventies)
Diagnosis of infections
PROSPECTOR (Buchanan, et al. ~1980)
Ore and oil exploration
INTERNIST (Popple, mid eighties)
Internal medicine

4. Structure of expert systems

5. FEATURES OF EXPERT SYSTEMS
Problem solving in the area of expertise
Interaction with user during and after problem solving
Relying heavily on domain knowledge
Explanation: ability of explain results to user

6. REPRESENTING KNOWLEDGE WITH IF-THEN RULES
Traditionally the most popular form of knowledge representation in expert systems
Examples of rules:
if condition P then conclusion C
if situation S then action A
if conditions C1 and C2 hold then condition C does not hold

7. DESIRABLE FEATURES OF RULES
Modularity: each rule defines a relatively independent piece of knowledge
Incrementability: new rules added (relatively independently) of other rules
Support explanation
Can represent uncertainty

8. TYPICAL TYPES OF EXPLANATION
How explanation
Answers user’s questions of form: How did you reach this conclusion?
Why explanation
Answers users questions: Why do you need this information?

9. RULES CAN ALSO HANDLE UNCERTAINTY
If condition A then conclusion B with certainty F

10. EXAMPLE RULE FROM MYCINif
1 the infection is primary bacteremia, and
2 the site of the culture is one of the sterilesites, and
3 the suspected portal of entry of the organism is the
gastrointestinal tract
then
there is suggestive evidence (0.7) that the identity of the organism is bacteroides.

11. EXAMPLE RULES FROM AL/X
Diagnosing equipment failure on oil platforms, Reiter 1980 if
the pressure in V-01 reached relief valve lift pressure
then
the relief valve on V-01 has lifted [N = 0.005, S = 400]
NOT the pressure in V-01 reached relief valve lift pressure,
and the relief valve on V-01 has lifted
the V-01 relief valve opened early (the set pressure has drifted)
[N = 0.001, S = 2000]

12. EXAMPLE RULE FROM AL3 Game playing, Bratko 1982if
1 there is a hypothesis, H, that a plan P succeeds, and
2 there are two hypotheses,
H1, that a plan R1 refutes plan P, and
H2, that a plan R2 refutes plan P, and
3 there are facts: H1 is false, and H2 is false
then
1 generate the hypothesis, H3, that the combined
plan ‘R1 or R2' refutes plan P, and
2 generate the fact: H3 implies not(H)

13. A TOY KNOWLEDGE BASE: WATER IN FLAT
Flat floor plan
Inference network

14. IMPLEMENTING THE ‘LEAKS’ EXPERT SYSTEM
Possible directly as Prolog rules:
leak_in_bathroom :-
hall_wet,
kitchen_dry.
Employ Prolog interpreter as inference engine
Deficiences of this approach:
Limited syntax
Limited inference (Prolog’s backward chaining)
Limited explanation

15. TAILOR THE SYNTAX WITH OPERATORS
Rules: if
hall_wet and kitchen_dry
then
leak_in_bathroom.
Facts:
fact( hall_wet).
fact( bathroom_dry).
fact( window_closed).

16. BACKWARD CHAINING RULE INTERPRETER
is_true( P) :-
fact( P).
if Condition then P, % A relevant rule
is_true( Condition) % whose condition is true
is_true( P1 and P2) :-
is_true( P1),
is_true( P2).
is_true( P1 or P2) :-
is_true( P1) ; is_true( P2).

17. A FORWARD CHAINING RULE INTERPRETER
new_derived_fact( P), % A new fact !,
write( 'Derived: '), write( P), nl,
assert( fact( P)),
forward % Continue ;
write( 'No more facts') % All facts derived

18. FORWARD CHAINING INTERPRETER, CTD.
new_derived_fact( Concl) :-
if Cond then Concl, % A rule
not fact( Concl), % Rule's conclusion not yet a fact
composed_fact( Cond) % Condition true?
composed_fact( Cond) :-
fact( Cond) % Simple fact
composed_fact( Cond1 and Cond2) :-
composed_fact( Cond1),
composed_fact( Cond2) % Both conjuncts true
composed_fact( Cond1 or Cond2) :-
composed_fact( Cond1) ; composed_fact( Cond2).

19. FORWARD VS. BACKWARD CHAINING
Inference chains connect various types of info.:
data goals
evidence hypotheses
findings, observations explanations, diagnoses
manifestations diagnoses, causes
Backward chaining: “goal driven”
Forward chaining: “data driven”

20. FORWARD VS. BACKWARD CHAINING
What is better?
Checking a given hypothesis: backward more natural
If there are many possible hypotheses: forward more natural (e.g. monitoring: data-driven)
Often in expert reasoning: combination of both
e.g. medical diagnosis
water in flat: observe hall_wet, infer forward leak_in_bathroom, check backward kitchen_dry

21. GENERATING EXPLANATION
How explanation: How did you find this answer?
Explanation = proof tree of final conclusion
E.g.: System: There is leak in kitchen.
User: How did you get this?

22. % is_true( P, Proof) Proof is a proof that P is true
:- op( 800, xfx, <=).
is_true( P, P) :-
fact( P).
is_true( P, P <= CondProof) :-
if Cond then P,
is_true( Cond, CondProof).
is_true( P1 and P2, Proof1 and Proof2) :-
is_true( P1, Proof1),
is_true( P2, Proof2).
is_true( P1 or P2, Proof) :-
is_true( P1, Proof) ;
is_true( P2, Proof).

23. WHY EXPLANTION Exploring “leak in kitchen”, system asks:
Was there rain?
User (not sure) asks:
Why do you need this?
System:
To explore whether water came from outside
Why explanation = chain of rules between current goal and original (main) goal

24. UNCERTAINTY Propositions are qualified with:
“certainty factors”, “degree of belief”, “subjective probability”, ...
Proposition: CertaintyFactor
if Condition then Conclusion: Certainty

25. A SIMPLE SCHEME FOR HANDLING UNCERTAINTY
c( P1 and P2) = min( c(P1), c(P2))
c(P1 or P2) = max( c(P1), c(P2))
Rule “if P1 then P2: C” implies: c(P2) = c(P1) * C

26. DIFFICULTIES IN HANDLING UNCERTAINTY
In our simple scheme, for example:
Let c(a) = 0.5, c(b) = 0, then c( a or b) = 0.5
Now, suppose that c(b) increases to 0.5
Still: c( a or b) = 0.5
This is counter intuitive!
Proper probability calculus would fix this
Problem with probability calculus: Needs conditional probabilities - too many?!?

27. TWO SCHOOLS RE. UNCERTAINTY
School 1: Probabilities impractical! Humans don’t think in terms of probability calculus anyway!
School 2: Probabilities are mathematically sound and are the right way!
Historical development of this debate: School 2 eventually prevailed - Bayesian nets, see e.g. Russell & Norvig 2003 (AI, Modern Approach), Bratko 2001 (Prolog Programming for AI)