1. Rule-Based Expert Systems
CPS 4801

2. About the midterm exam Exam on March 13 Tuesday (Tentatively)
Review on March 8 Thursday
Grades will be out by March 15, before spring break.

3. Strong AI vs. Weak AI
Strong AI is artificial intelligence that matches or exceeds human intelligence.
“Artificial general intelligence”
The weak AI hypothesis: machines can demonstrate intelligence, but do not necessarily have a mind, mental states or consciousness.

4. “General purpose” intelligence vs. Domain-specific intelligence
Understand how the world works in general
requires vast amounts of knowledge about the world.
Domain-specific intelligence
Restricted to a particular domain
Knowledge is deep, but not wide.
Avoids the world knowledge problem, and is much more feasible for implementation.

5. Expert Systems
Domain expert: A person who has deep knowledge (in the form of facts and rules) and strong personal experience in a particular domain.
An expert system performs at a human expert level in a narrow and specialized domain.

6. Medical Diagnostics simple expert system

7. DXplain DXplain first launched in 1986
Users enter clinical information, then ask DXplain to provide diagnostic decision support
DXplain knowledge base (KB) covers ~2400 diseases and over 5000 clinical findings (signs, symptoms, epidemiologic data, laboratory findings, etc.)
Demo:
Info:

8. GIDEON Global Infectious Disease and Epidemiology Network
Online application that supports the diagnosis and treatment of infectious diseases
Organized by country of origin
Updated weekly
Info:

9. Characteristics of Expert Systems
Often a tradeoff between accuracy and speed.
Expert systems apply heuristics to guide the reasoning process.

10. Reasoning + Knowledge + Facts
Human expertise typically breaks down into:
Ability to reason
Knowledge about the domain
Facts about the particular situation (e.g. this patient’s symptoms)
Expert Systems use symbolic reasoning to solve problems
Symbols represent facts and rules (i.e. knowledge)

11. Characteristics of Expert Systems
Expert systems provide explanation facilities to display reasoning to users.
How did you come to that conclusion or diagnosis?
E.g., Why do you think I have a migraine?
Well, you have frequent, intense pain in the temple area, associated with nausea. Also, you aren’t taking any medications that are likely to produce these symptoms.

12. Characteristics of Expert Systems
Expert systems make mistakes
So do human experts!
Users have to be aware of this possibility

13. Rules Production rules (most commonly used type)
IF the ‘traffic light’ is green
THEN the action is go
IF the ‘traffic light’ is red
THEN the action is stop

14. Rules
Any rule consists of two parts: the IF part, called the antecedent (premise or condition) and the THEN part called the consequent (conclusion or action).
IF <antecedent>
THEN <consequent>
Alternate syntax:
<antecedent>  <consequent>

15. Multiple antecedents
A rule can have multiple antecedents joined by the keywords AND (conjunction), OR (disjunction) or a combination of both.
IF animal is horse-shaped
AND animal has stripes
THEN animal is zebra
IF animal is hippo
OR animal is lion
THEN animal is dangerous

16. Multiple consequents
Multiple consequents are possible, and are connected by conjunctions.
IF tsunami alarm is sounding
AND date is not first Monday in month
THEN Condition is dangerous
AND Advice is “move away from the ocean”

17. Structure of antecedents and consequents
The antecedent of a rule incorporates two parts: an object and its value. The object and its value are linked by an operator.
Operators
is, are, is not, are not are used to assign a symbolic value to a linguistic object.
mathematical operators to define an object as numerical and assign it to the numerical value.
IF ‘age of the customer’ < 18
AND ‘cash withdrawal’ > 1000
THEN ‘signature of the parent’ is required

18. Semantics of rules
Relation IF the ‘fuel tank’ is empty THEN the car is dead
Recommendation IF the season is autumn AND the sky is cloudy AND the forecast is drizzle THEN the advice is ‘take an umbrella’
Directive IF the car is dead AND the ‘fuel tank’ is empty
THEN the action is ‘refuel the car’

19. Strategy IF the car is dead THEN the action is ‘check the fuel tank’; step1 is complete
IF step1 is complete AND the ‘fuel tank’ is full THEN the action is ‘check the battery’; step2 is complete
Heuristic IF the spill is liquid AND the ‘spill pH’ < AND the ‘spill smell’ is vinegar THEN the ‘spill material’ is ‘acetic acid’

20. So what are facts?

21. Rule-based expert system
An expert system whose knowledge base (KB) contains a set of production rules.

22. The Main Players In The Expert System Development Team

23. Structure of a rule-based expert system

24. Inference Engine domain knowledge: IF-THEN rules
data: facts about the current situation
When the IF (condition) part of the rule matches a fact, the rule is fired.
The matching of the rule IF parts to the facts produces inference chains (new facts are discovered).

26. Inference engine algorithm
Inference engine compares each rule with facts it already “knows” about, matching the antecedent (IF condition)
When the antecedent matches one or more known facts, the rule fires and its consequent (THEN) is executed

27. Inference Chain
An inference chain indicates how an expert system applies rules to reach a conclusion
given: A, B, D, E

28. Inference Chain
An inference chain indicates how an expert system applies rules to reach a conclusion
given: A, B, D, E

29. Inference Chain
An inference chain indicates how an expert system applies rules to reach a conclusion
given: A, B, D, E

30. Two approaches
Forward chaining
Backward chaining

31. Forward chaining (data-driven search)
Starts with known data (facts).
Fire the rules that have an antecedent that matches facts in the database, and add any resulting facts to the database.
Each rule can fire only once.
Each time only the topmost rule is executed.
When no more rules can fire, stop.

32. Forward Chaining

33. Forward Chaining Exercise 1
Use forward chaining to prove the following:

34. Forward Chaining Exercise 1
Use forward chaining to prove the following:
Facts:
Rules fired:
A B C D E X Y Q
A B C D E X Y Q R
A B C D E X Y Q R S T
A B C D E X Y
A B C D E X Y Q R S
A B C D E X Y Q R S T Z
A B C D E
A B C D E X
Proven:
A  X
A  Y
A & D  Q
Q  R
R  S
Q  T
T  Z Z

35. Forward Chaining Exercise 2
Use forward chaining to prove the following:

36. Forward Chaining Exercise 2
Use forward chaining to prove the following:
Facts:
Rules fired:
A B C D E X Y Q R S T Z
Cannot prove:
No more rules left to fire
A  X
A  Y
A & D  Q
Q  R
R  S
Q  T
T  Z L

37. Forward Chaining +/-
Good for answering “What is the situation?” kind of questions (e.g. “What kind of animal is this?”)
Fires a lot of rules, and generates a lot of facts that might be irrelevant to the problem
If our goal is to infer only one particular fact, the forward chaining inference technique would not be efficient.

38. Backward chaining (goal-driven search)
System has hypothetical solution(s) (e.g. “The patient has type I diabetes”), and tries to prove it.
Find rules that consequents that match the goal.
If antecedents match the facts, stop.
If not, make the antecedents the new subgoals, and repeat.

39. Backward chaining algorithm
At the first iteration, rule(s) with the desired goal in the consequent are selected
Stack up and attain many subgoals until....
If the antecedent matches known data, the rule is fired and the goal is proven
Otherwise, if no rules remain, the desired goal is not proven

40. Backward chaining

41. Backward chaining

42. Backward chaining Exercise 1
Use backward chaining to prove the following:

43. Backward chaining Exercise 1
Use backward chaining to prove the following:
Facts:
Stack of rules: (subgoals)
Rules fired:
A B C D E Q T Z
A B C D E
Proven:
A & D  Q
Q  T
T  Z Z
A & D  Q
Q  T
T  Z

44. Backward chaining Exercise 2
Use backward chaining to prove the following:

45. Backward chaining Exercise 2
Use backward chaining to prove the following:
Facts:
Stack of rules: (subgoals)
Cannot prove:
Subgoal N cannot be proven
A B C D E
N  L L

46. Backward chaining +/-
Efficient way to prove or disprove a particular hypothesis.
Sometimes more efficient with a small set of hypotheses
Less efficient than forward chaining if large number of hypotheses

47. Forward vs. backward chaining
If an expert first needs to gather some information and then tries to infer from it whatever can be inferred, choose the forward chaining inference engine.
However, if your expert begins with a hypothetical solution and then attempts to find facts to prove it, choose the backward chaining inference engine.

48. Forward vs. backward chaining
Forward chaining: best for analysis and interpretation (e.g. DENDRAL (1971) determines molecular structure of soil sample).
Backward chaining: best for diagnosis (e.g. MYCIN (1976) diagnoses infectious blood diseases).

49. Forward + backward chaining
Most real expert systems use both.
Primary inference is backward chaining.
Switches to forward chaining when new data is added.
Minimizes pointless queries to user (backward chaining), but exploits any facts that are acquired.

50. Conflict resolution
Rules with identical antecedents (IF conditions) can cause conflicts via their consequents (THEN clauses)
Rule 1:
IF the ‘traffic light’ is green THEN the action is go
Rule 2:
IF the ‘traffic light’ is red THEN the action is stop
Rule 3:
IF the ‘traffic light’ is red THEN the action is go

51. Conflict sets
A subset of the rules in a knowledge base that can fire at the same time, but have inconsistent consequents.
X is dog  X is not smart
X is dog & X has breed = “border collie” 
X is smart

52. Conflict resolution
Conflict resolution provides a specific method for choosing which rule to fire.
Highest priority
Most specific rule
Most recent first

53. Conflict resolution methods (1)
Rules fire one at a time. But which fires first?
Order of rules determines order of firing.
Give rules explicit priority.
In simple applications, the priority can be established by placing the rules in an appropriate order in the knowledge base.

54. Conflict resolution methods (2)
Fire the most specific rule (longest matching strategy).
Assumes that a specific rule processes more info than a general one.
X is dog  X is not smart
X is dog & X has breed = “border collie” 
X is smart

55. Conflict resolution methods (3)
Fire the rule based on the data most recently entered in the database
Assumes that recent data is more important than older data.
Relies on time tags attached to each fact in the database.
R1: …… [08:16 PM 02/27/2012]
R2: …… [10:18 AM 02/28/2012]

56. Pros of rule-based expert systems
Natural knowledge representation
Represent knowledge in near-linguistic, declarative manner that is close to how experts explain their own reasoning.
Uniform structure
uniform IF-THEN structure
Separation of knowledge from processing.
Good at handling incomplete or uncertain knowledge (next topic).

57. Cons of rule-based expert systems
Opaque relations between rules
How do the rules relate to each other?
Difficult to avoid conflicts in large knowledge bases.
Ineffective search strategy
The inference engine applies an exhaustive search through all the rules during each cycle.
unsuitable for real-time applications
Unable to learn
An expert system cannot automatically modify its knowledge base, adjust existing rules or add new ones.