1. Rule-based representation
Example
IF GMAT score >= 600
THEN Admit student to MBA program
ELSE do not admit student.
Example (conjunctive condition clauses)
IF sky is clear
AND temperature is low
THEN chance of frost is high
Example (disjunctive condition clauses)
IF age of car is new
OR condition of car is good
THEN cat should start
AND trip should be safe OR in THEN-part? 1

2. Rules Example AND loan >= 10,000 AND (loan >= 10,000
IF interest_rate > 10 IF interest_rate > 10
AND loan >= 10,000 AND (loan >= 10,000
OR due_date = today OR due_date = today)
THEN review_loan = True. THEN review_loan = True.
OR has precedence / left-to-right evaluation
A rule’s premise is true if it has been testes and premise is satisfied.
If premise is true, the rule is said to be triggered.
A rule fires implies that the action specified by the conclusion clauses is taken. 2

3. Multiple rules Rule 1: IF A = x Assume facts: OR B = y A = x
THEN D = d1 B = not y
Rule 2: IF A = x C = not Z
AND C = z Which rule fires?
AND B = NOT y
THEN D = d2
Facts: A = x, C = z, B = not y. Which rule fires?
Multiple values for a variable?
Symptoms for a disease
In presence of uncertainty 3

4. Inference Techniques Deductive Reasoning
Combines facts (axioms) with general knowledge in the form of implications to conclude new facts.
Example: Axiom: I am sleeping in class
Implication: Sleeping in class => Rude shock in exams
Conclusion: I will get a rude shock in the exam.
Modus ponens
IF A is true, and if A => B, then B is true. 4

5. Inference techniques Inductive reasoning Abductive Reasoning
Generalizing from specific facts.
case 1: Game on 21st Sept. (Friday), It rained, We lost.
case 2: Game on 5th Nov. (Friday), It rained, We lost.
…..
Induce general rule: If game is on a Friday AND it rains
Then we lose.
Abductive Reasoning
Deduction with plausible implications
IF B is true, and A=>B, then A is true. 5

6. Inference technqiues Monotonic Reasoning Non-monotonic reasoning
facts remain static over period of problem-solving
Non-monotonic reasoning
facts need to remain static over period of problem-solving.
Truth Maintenance Systems used for non-monotonic reasoning. 6

7. Inference: Forward Chaining
Example
R1: IF A and C THEN E Given facts:
R2: IF D and C THEN F A is true
R3: IF B and E THEN F B is true
R4: IF B THEN C What can be concluded?
R5: IF F THEN G
Cycle through rules, looking for rules whose premise matches the working memory. Working memory
A, B
R4 fires: assert new fact C A, B, C
R1 fires: assert new fact E A, B, C, E
R3 fires: assert new fact F A, B, C, E, F
R5 fires: assert new fact G A, B, C, E, F, G
Concludes everything possible from available information 7

8. Inference Recognize-Resolve-Act cycle Recognize Resolve Act
match rule premises to facts in working memory,
identify rules that can fire
Resolve
if more than one rules can fire, choose one rule
Act
fire the chosen rule and add its conclusion to working memory 8

9. Forward Chaining: Example
R1: IF the patient has a sore throat
AND we suspect a bacterial infection Facts:
THEN we believe the patient has strep throat Patients temperature=102
R2: IF the patient’s temperature is > Has been sick for 2 months
THEN the patient has fever Has a sore throat
R3: IF the patient has been sick for over a month
AND the patient has fever
THEN we suspect a bacterial infection
Cycle 1: Consider R1, R2, R3
R2 fires: assert patient has fever
Cycle 2: consider R1, R3
R3 fires: assert bacterial infection
Cycle 3: R1 fires: assert strep throat
Data driven reasoning: will fire all rules possible, can continue reasoning about irrelevant details. 9

10. Inference: Backward chaining
Attempts to prove a hypothesis (goal) by gathering supporting information
Example
R1: IF B and C THEN G R4: IF E or F THEN C
R2: IF A and G THEN I R5: IF D and C THEN K
R3: IF D and G THEN J
Goal: I
Goal I: need to trigger R2 Subgoal C: Need to trigger R4
Need both A and G Need E or F
Subgoal A: need user input (ask user) Subgoal E: need user input (ask user)
Subgoal G: Need to trigger R1
Need both B and C
Subgoal B: need user input (ask user) 10

11. Combining Forward & Backward Chaining
Separate systems
separate expert-systems for different sub-tasks
one sub-system solves part of problem, then passes control to another
Demon Rules
A rule that fires whenever its premises match contents of the working memory 1

12. Demon Rules
demon rules amongst backward chaining rules, but they do not participate in the normal backward-chaining inference.
A demon rule remains dormant until information in premises appears in working-memory; then the rule fires.
The firing on one demon-rule can cause other demon rules to fire -- like a series of forward chaining rules in an otherwise backward chaining process. 2

13. Demon rules: Example Demon 1 Tank pressure Problem IF Power of off
AND Tank Pressure > 1000
THEN Problem = Tank Pressure Problem
Demon 2 Emergency Situation
IF Problem = Tank Pressure Problem
THEN Situation = Emergency
Demon 3 Evacuate
IF Situation = Emergency
THEN Response = Evacuate Personnel 3

14. Specifying Rules
(Variable, value) variable = value, variable <= value
variable IS value
variable IS NOT value
Example
IF car = red
AND car = old
AND cat = automatic What is car here?
THEN car = sid
Object, Attribute, Value
Object: car
Attributes: Values
car_color: red, white, green
car_age: old, new
car_transmission automatic, stick
car_owner sid, frog, ... 11

15. Object, Attribute, Value
Example
IF GPA equal or exceeds 3.5
THEN accept into honor society
Attribute: GPA Attribute: accept into honor society
Value: (student_acceptance_status)
Object: Student (implicit) Value: yes
IF client is risk adverse
THEN invest in bluechip stock
Object: Client
Attribute: client risk profile
Attribute: client investment strategy 12