Artificial Intelligence Notes Unit 2

UNIT-2
KNOWLEDGE REPRESENTATION
Prepared by- Agniwesh Mishra, Rungta
College of Engg. & Tech. , Bhilai

SYLLABUS
• Introduction to KR, Knowledge agent, Predicate logic, WFF,
• Inference rule & theorem proving forward chaining, backward
chaining,
• resolution;
• Propositional knowledge,
• Boolean circuit agents.
• Rule Based Systems,
• Forward reasoning: Conflict resolution,
• backward reasoning: Use of Back tracking,
• Structured KR: Semantic Net - slots,
• inheritance,
• Frames- exceptions and defaults attached predicates,
• Conceptual Dependency formalism and other knowledge
representations. Prepared by- Agniwesh Mishra, Rungta

Knowledge Representation
• A knowledge representation is a study of ways of how
knowledge is actually represented and how effectively
it resembles the representation of knowledge in
human brain.
• A knowledge representation system should provide
ways of representing complex knowledge.
• How to represent the knowledge in a machine?
• So to represent the knowledge we need a language.
There must be a method to use this knowledge.
• Syntax and semantic must be well defined in order to
represent the language.

Characteristics of Knowledge Representation
i. The representation scheme should have a set of well-
defined syntax and semantic. This will help in
representing various kinds of knowledge.
ii. The knowledge representation scheme should have a good
expressive capacity. A good expressive capability will
catalyze the inference mechanism in its reasoning process.
iii. From the computer system point of view, the
representation must be efficient. By this we mean that it
should use only limited resources without compromising
on the expressive power.Prepared by- Agniwesh Mishra, Rungta

Fact and Fact Representation
• Fact: Truth in some relevant world. These are the things
we want to represent.
• Representation of fact in some chosen formalism. These
are the things we will actually be able to manipulate.

Representation schemes
i. Propositional logic
ii. Semantic Networks
iii. Frame
iv. Conceptual dependency
v. Scripts

Propositional Logic

Proposition
• Propositions are elementary atomic sentences.
• Propositions may be either true or false but may take on
no other value.
• There are two kinds of proposition
– Simple
– compound
• Some examples of simple propositions are
– It is raining.
– My car is painted silver.
– John and sue have five children.
– Snow is white.
– People live on the moon.

Proposition continued….
• Compound propositions are formed from
atomic formulas using the logical connectives
not, or, if ………then, and, if and only if.
• For example, the following are compound
formulas.
– It is raining and the wind is blowing.
– The moon is made of green cheese or it is not.
– If you study hard you will be rewarded.
– The sum of 10 and 20 is not 50.

Logic
• One of the prime activities of the human intelligence is
reasoning.
• The activity of reasoning involves construction, organization
and manipulation of statements to arrive at new conclusions.
• Thus logic can be defined as a scientific study of the process
of reasoning.
• Logic is a formal language.
• Logic is basically classified in two main categories
– Propositional logic
– Predicate logic

Propositional logic
• Propositional logic is a representational
language that makes the assumption that the
world can be represented solely in terms of
propositions that are true or false.
• One of the main concerns of propositional
logic is the study of rules by which new logic
variables can be produced as functions of
some given logic variables.

Syntax for Propositional logic
1. Logic constants: true, false
2. Propositional atoms: indivisible propositions A,B,C,… P,Q,R.
3. Connectives: negation, conjunctive, disjunctive, implication,
equivalence
4. Sentences (well-formed formulas): wff

Connectives

Well-formed formulas (wff)
• WFF consist of atomic symbols joined with
connectives.
• Examples: P, P ʌ ~Q, P v Q , P Q

Two Normal (Canonical) Forms
All wffs can be expressed in the following to normal forms
1. CNF (Conjunctive Normal Form)
C)D(BB)(A e.g.:
Clause 1 clause 2
e.g.: C)D(BB)(A 
models models
2. DNF (Disjunctive Normal Form)

Semantics for Propositional logic
The semantics or meaning of a sentence is just the value true
or false. The terms used for semantics of a language are given
below.

Example
Write the syntax for propositional logic
If the road is closed, then the traffic is blocked.
“the road is closed” is represented by a proposition, P .
“then the traffic is blocked” is represented by a proposition, Q .
The sentence is represented as

Truth tables for logical connectives

Show that

Show that
P <-> Q is equivalent to

Show that
P -> Q is equivalent to ~P  (P Q)

Truth table for equivalent sentences

Some Equivalence Laws

Question 1
• Determine whether each of the following
sentences is (a) Satisfiable (b) Contradictory and
(c) Valid.
S1: (P & Q)  ~(P  Q)
S2: (P  Q)-> (P & Q)
S3: (P & Q) -> (P  ~Q)
S4: (P  Q) & (P  ~Q)  P
S5: P -> Q->~P
S6: P  Q & ~P  ~Q &P

S1
S1: (P & Q)  ~(P  Q) satisfiable

S2
S2: (P  Q)-> (P & Q) satisfiable

S3
S3: (P & Q) -> (P  ~Q) valid

S4
S4: (P  Q) & (P  ~Q)  P satisfiable

S5
S5: P -> Q->~P satisfiable

S6
S6: P  Q & ~P  ~Q &P satisfiable

Rules of inference
• The inference rules of propositional logic provide the means
to perform logical proofs or deductions.
• Rules are
i. Modus ponens
ii. Modus tollens
iii. Chain rule
iv. Substitution
v. Simplification
vi. Conjunction
vii. Transposition

Inference Rules cont………

Inference Rules cont………
Modus tollens
• α → β
¬β
¬α
Example:
Given: The machine is defective(α )->the production is less (β)
And : The production is not less (¬β)
Conclude: The machine is not defective (¬α)

Forward Chaining
• An inference engine using forward chaining searches the inference rules
until it finds one where the IF clause is known to be true.
• When found it can conclude, or infer, the THEN clause, resulting in the
addition of new information to its dataset. In other words, it starts with
some facts and applies rules to find all possible conclusions. Therefore, it is
also known as Data Driven Approach.
• The standard definition of a forward-chaining system is that the system
operates by repeating the following sequence of operations
1. Examine the rules to find one who‟s If part is satisfied by the current
contents of Working Memory.
2. Fire the rule by adding to Working Memory the facts that are specified in the
rules Then part. (The Then part may perform other actions as well, but that
can be ignored for now.) This control cycle continues until no rules have
satisfied If parts.

Flowchart for forward chaining

Backward chaining
• An inference engine using backward chaining would search
the inference rules until it finds one which has a THEN
clause that matches a desired goal. If the IF clause of that
inference rule is not known to be true, then it is added to
the list of goals (in order for goal to be confirmed it must
also provide data that confirms this new rule) . In other
words, this approach starts with the desired conclusion and
works backward to find supporting facts. Therefore, it is
also known as Goal-Driven Approach.
• Backward-chaining systems try to satisfy the goals in the
goal stack. They do this by finding rules that can conclude
the information needed by the goal, and trying to make the
If parts of those rules satisfied . In more detail, the standard
backward-chaining control cycle is shown in figure.

Steps for backward chaining
1. Check the conclusions of the rules to find all rules
that can satisfy the top goal on the stack. 2. Process
these rules one at a time:
a. Evaluate the conditions in the rules If part one at a
time:
i. If the condition is currently unknown (that is, if there is
not enough information currently known to determine
whether the condition is satisfied) push a goal to make
that condition known, and recursively invoke the system.
ii. If the condition is known to be unsatisfied, continue with
the loop at Step 2.
iii. If it was not possible to determine whether the condition
was satisfied, continue with the loop at Step 2.

Steps for backward chaining
b. If all the conditions in the selected rule are
satisfied, add to Working Memory the facts
specified in the Then part of the rule, pop the
goal off the stack, and return from this
invocation of the system. The system
terminates with success when the goal stack is
empty. It terminates with failure if the system
runs out of rules to try in Step 2.

Flowchart for backward chaining

First Order Predicate Logic (FOPL)
• FOPL was developed to extend the expressiveness of
Propositional logic.
• Propositional logic works fine in situations where the
result is either true or false. However, there are many real
life situations that cannot be treated this way.
• Predicate logic is the area of logic that deals with
predicates and quantifiers
• Predicate – refers to a property that the subject of a
statement can have
E.G. “x is greater than 3”
x  is the subject and
“is greater than 3”  is the predicate

Limitations of Propositional Logic
• The propositional logic has its limitations that you cannot deal
properly with general statements of the form
• “All men are mortal”
• You can not derive from the conjunction of this and “Socrates
is a man” that “Socrates is mortal”
Example:
• If All men are mortal = P
• Socrates is a Man = Q
• Socrates is mortal = R
• Then (P & Q)  R is not valid

Syntax of FOPL
• Connectives
• Quantifiers
• Constants
• Variables
• Predicates
• Functions

Syntax of FOPL
• Connectives: There are five connective symbols
– ~ : not or negation
– & : and or conjuction
–  : or or inclusive disjunction
–  : implication
–  : equivalence or if and only if
• Quantifiers: There are two quantifier symbols are
–  : existential quantification
Example: x means there exist x
–  : universal quantification
Example: x means for all x

• Constants: constants are fixed-values terms that belong to a
given domain of discourse. They are denoted by numbers,
words and small letter.
• Variables: variables are terms that can assume different values
over a given domain.
Syntax of FOPL...

Syntax of FOPL...
• Predicates: a predicate is defined as a relation that binds
two atoms together. Ex: Rabbit likes carrots, is represented
as
LIKES(rabit, carrots)
Here LIKES is a predicate that links two atoms
“rabbit“ and “carrots“
• Functions: it is also possible to have a function as an
argument, e.g. “ Ravi„s father is Rani„s father“ is
represented as-
– FATHER(father(Ravi), Rani)
– Here FATHER is a predicate and father(Ravi) is a function to
indicate Ravi„s father.

Syntax of FOPL...
• Constants, variables, and functions are referred
to as terms.
• Predicates are referred to as atomic formulas or
atoms.
• When we want to refer to an atom or its
negation, we often use the word literal.
• In addition to above symbols, left and right
parentheses , square brackets, braces, and the
period are used for punctuation in symbolic
representation.

Translating English to FOPL
1. Bhaskar likes aeroplanes.
2. Ravi‟s father is rani‟s
father.
3. Plato is a man
4. Ram likes mango.
5. Sima is a girl.
6. Rose is red.
7. John owns gold
8. Ram is taller than mohan
9. My name is khan
10. Apple is fruit.

11. Ram is male.
12. Tuna is fish.
13. Dashrath is ram‟s father.
14. Kush is son of ram.
15. Kaushaliya is wife of Dashrath.
16. Clinton is tall.
17. There is a white alligator.
18. All kings are person.
19. Nobody loves john.
20. Every one has a father.

1. Bhaskar likes aeroplanes.
2. Ravi‟s father is rani‟s
father.
3. Plato is a man.
4. Ram likes mango.
5. Sima is a girl.
6. Rose is red.
7. John owns gold.
8. Ram is taller than mohan.
9. My name is khan.
10. Apple is fruit.
Likes (bhaskar, aeroplanes).
Father(father(ravi), rani )).
Man (plato).
Likes(ram, mango).
Girl(sima) .
Red (rose).
Owns(john, gold).
Taller(ram, mohan).
Name (khan) or Name(my, khan)
Fruit(apple).

11. Ram is male.
12. Tuna is fish.
13. Dashrath is ram‟s father.
14. Kush is son of ram.
15. Kaushaliya is wife of Dashrath.
16. Clinton is tall.
17. There is a white alligator.
18. All kings are person.
19. Nobody loves john.
20. Every one has a father.
Male (ram).
Fish (tuna).
Father (dashrath, ram).
Son(kush, ram).
Wife(kaushaliya, dashrath).
Tall(clinton).
Alligator (white).
x: Kings(x)  Person(x).
x: ¬Loves(x, john).
x: y: Father(y,x)

Translate into predicate logic
1. Marcus was a man.
2. Marcus was a Pompeian.
3. All Pompeians were Romans.
4. Caesar was a ruler.
5. All Romans were either loyal to Caesar or hated him.
6. Everyone is loyal to someone.
7. People only try to assassinate rulers they aren't loyal to.
8. Marcus tried to assassinate Caesar.
9. All men are people.

Solution
 Man(Marcus).
 Pompeian(marcus)
3. All Pompeian were Romans.
 x: Pompeian(x)  Roman(x)
 Ruler(Caesar)
 x: Roman(x)  LoyalTo(x,Caesar)  Hate(x,Caesar)

Solution
x: y: LoyalTo(x,y)
x: y: Person(x) ^ Ruler(y) ^ TryAssassinate(x,y) ¬LoyalTo(x,y)]
TryAssassinate(Marcus, Caesar)
x: Men(x)  People(x)

Translate into predicate logic
i. Hari likes all kind of food.
ii. Bananas are food.
iii. Apples are food.
iv. Anything anyone eats and isn‟t killed by food.
v. Hari eats everything ram eats.

Solution
i. x: Food(x)  Likes(hari, x).
ii. Food(bananas) .
iii. Food(apples) .
iv. x: y: Eats(y,x) ^ ¬Killedby(y,x) Food(x)
v. x: eats(ram, x)  eats(hari, x) .

1. Every gardener likes the sun.
2. Not Every gardener likes the sun.
3. You can fool some of the people all of the time.
4. You can fool all of the people some of the time.
5. You can fool all of the people at same time.
6. You can not fool all of the people all of the time.
7. Everyone is younger than his father.

Solution
1. (x): gardener(x) => likes(x, Sun)
2. ~((x) :gardener(x) => likes(x, Sun))
3. (x):(t) :person(x) ^ time(t) => can-be-fooled(x, t)
4. (x):(t) :person(x) ^ time(t) => can-be-fooled(x ,t)
5. (t):(x) :person(x) ^ time(t) => can-be-fooled(x, t)
6. ~((x):(t): person(x) ^ time(t) => can-be-fooled(x, t))
7. (x) :person(x) => younger(x, father(x))

1. All purple mushrooms are poisonous.
2. No purple mushroom is poisonous.
3. There are exactly two purple mushrooms.
4. Clinton is not tall.
5. X is above Y if X is directly on top of Y or there is a pile
of one or more other objects directly on top of one another
starting with X and ending with Y.
6. no one likes everyone

Solution
1. (x): (mushroom(x) ^ purple(x)) => poisonous(x)
2. ~(x): purple(x) ^ mushroom(x) ^ poisonous(x)
(x): (mushroom(x) ^ purple(x)) => ~poisonous(x)
3. (x):(y): mushroom(x) ^ purple(x) ^ mushroom(y) ^
purple(y) ^ ~(x=y) ^
4. ~tall(Clinton)
5. (z) :(mushroom(z) ^ purple(z)) => ((x=z) v (y=z))
(x):(y): above(x,y) <=> (on(x,y) v (z) (on(x,z) ^
above(z,y)))
6. ~ (x):(y):likes(x,y) or (x):(y):~likes(x,y)

Skolemization
• Skolemization is the process of replacement of
existential quantified variable with Skolem function and
deletion of the respective quantifiers.
• Skolem function is arbitrary functions which can always
assume a correct value required of an existentially
quantified variable.
• Example
– x : President(x)
– Can be transformed into the formula
– President(P1)
– Where P1 is a function with no arguments that
somehow produces a value that satisfies president.

Example 1
Everybody loves somebody.
x:  y: (Person(x) ^ Person(y)) Loves(x,y)]
Converted to
x: (Person(x) ^ Person f(x)) Loves(x,f(x))]
Where f(x) specifies the person that x loves.

Clausal Form
• A formula is said to be in clausal form if it is of the form:
∀x1 ∀x2 … ∀xn [C1 ∧ C2 ∧ … ∧ Ck]. „
• All first-order logic formulas can be converted to clausal
form.

Equivalent Logical Expressions
i. ~(~F) = F (Double Negation)
ii. F & G = G & F, F V G = G V F (Commutativity)
iii. (F & G) & H = F & (G & H), (F V G) V H = F V (G V H) (Associativity)
iv. F V (G & H) = (F V G) & (F V H), F & (G V H) = (F & G) V (F & H)
(Distributivity)
v. ~(F & G) = ~F V ~G, ~(F V G) = ~F & ~G (De Morgan)
vi. F  G = ~F V G
vii. F  G = (~F V G) & (~G V F)
viii. x F[x] V G = x (F[x] V G )
ix. x F[x] V G = x (F[x] V G )
x. x F[x] & G = x (F[x] & G )
xi. x F[x] & G = x (F[x] & G )

Equivalent Logical Expressions...
xii. ~(x) F[x] = x (~F[x])
xiii. ~(x) F[x] = x (~F[x])
xiv. x F[x] & x G[x] = x (F[x] & G[x])
xv.  x F[x] &  x G[x] =  x (F[x] & G[x])

Conversion to Clausal form or
Conjunctive Normal Form (CNF)
1. Eliminate logical implications, ⇒, using the fact that A ⇒ B is equivalent to ¬A ∨ B.
2. Reduce the scope of each negation to a single term, using the following facts:
¬(¬P) = P
¬(A ∨ B) = ¬A ∧ ¬B
¬(A ∧ B) = ¬A V ¬B
¬∀x: P(x) = ∃x: ¬P(x)
¬∃x: P(x) = ∀x: ¬P(x)
3. Standardize variables so that each quantifier binds a unique variable.
4. Move all quantifiers to the left, maintaining their order.
5. Eliminate existential quantifiers, using Skolem functions (functions of the preceding
universally quantified variables).
6. Drop the prefix; assume universal quantification.
7. Convert the matrix into a conjunction of disjunctions. [(a &b) or c=(a or c) & (b or c)
8. Create a separate clause corresponding to each conjunction.
9. Standardize apart the variables in the clauses.

Example of Clausal Conversion
• x y (z P(f(x), y, z)  (u Q(x, u) & v R(y, v)))
• Step 1: Eliminate logical implications
– x y (~(z) P(f(x), y, z) V (u Q(x, u) & ( v) R(y, v)))
• Step 2: Reduce the scope of each negation to a single term
– x y (z ~P(f(x), y, z) V (u Q(x, u) & (v) R(y, v)))
• Step 3:
– It is not require here because quantifiers have different variable
assignments.
• Step 4: Move all quantifiers to the left
– x y z u v (~P(f(x), y, z) V (Q(x, u) & R(y, v)))
• Step 5: Skolem functions
– y(~P f(x) , y, g(y)) V Q(a, h(y)) & R(y, l(y))
• Step 6: Drop the prefix
– (~P f(x) , y, g(y)) V Q(a, h(y)) & R(y, l(y))

Example of Clausal Conversion
• Step 7: Convert the matrix into a conjunction of disjunctions
– ((~P f(x) , y, g(y)) V Q(a, h(y)) &( ~P f(x) , y, g(y)) V R(y, l(y)))
 Step 8:
– (~P f(x) , y, g(y)) V Q(a, h(y))
– ( ~P f(x) , y, g(y)) V R(y, l(y)))
 Step 9: Standardize apart the variables in the clauses

Translate into Clausal form
i. Hari likes all kind of food.
ii. Bananas are food.
iii. Apples are food.
iv. Anything anyone eats and isn‟t killed by food.
v. Hari eats everything ram eats.

Convert it into clausal form
i. x: Food(x)  Likes(hari, x).
ii. Food(bananas) .
iii. Food(apples) .
iv. x: y: Eats(y,x) ^ ¬Killedby(y,x) Food(x)
v. x: Eats(ram, x)  Eats(hari, x) .

Solution
i. ¬ Food(x)  Likes(hari, x).
ii. Food(bananas) .
iii. Food(apples) .
iv. ¬ Eats(y,x)  Killedby(y,x)  Food(x)
v. ¬ Eats(ram, x)  eats(hari, x) .

Convert it into FOPL
i. All lectures are determined.
x: Lecturer(x)  Determined(x)
ii. Any one who is determined and intelligent will give good
service.
x: Determined(x) ^ Intelligent(x)Givegoodservice(x)]
iii. Mary is an intelligent lecturer.
Lecturer(mary)
Intelligent(mary)

Convert FOPL into clausal form
i. All lectures are determined.
x: Lecturer(x)  Determined(x)
¬ Lecturer(x)  Determined(x)
i. Any one who is determined and intelligent will give good service.
x: Determined(x) ^ Intelligent(x)Givegoodservice(x)
¬ Determined(x)  ¬ Intelligent(x)  Givegoodservice(x)
iii. Mary is an intelligent lecturer.
Lecturer(mary)
Intelligent(mary)
(Both are same)

Horn clause
• It is a clause with at most one positive literal.
• Example
– P
– ~P  Q
– ~P  ~Q  R
Clauses of this type were first investigated by the logician Alfred Horn.
There are three types of Horn clauses
i. A single atom: often called a “fact”.
ii. An implication: often called a “rule”- whose antecedent consists of a
conjunction of positive literals and whose consequent consists of a
single positive literal.
iii. A set of negative literals: written in implication form with an
antecedent consisting of a conjunction of positive literals and an
empty consequent. This form is obtained, for example, when one
negats a wff to be proved consisting of a conjunction of positive
literals. Such a clause is therefore often called a “goal”.

Horn clause
• In a horn clause, generally one condition is followed by
zero, or more conditions. It is represented as
Conclusion..
Condition-1,
Condition-2,
Condition-3,
.
Condition-n,
The conclusion is true if and only if condition-1, condition-
2……and so on until condition –n is true.
In simple and easy terms, a Horn clause consists of a set of
statements joined by logical AND’s.

Example of FOPL
 Man(Marcus).
 Pompeian(marcus)
3. All Pompeian were Romans.
 x: Pompeian(x)  Roman(x)
 Ruler(Caesar)
 x: Roman(x)  LoyalTo(x,Caesar)  Hate(x,Caesar)

Example of FOPL
x: y: LoyalTo(x,y)
x: y: Person(x) ^ Ruler(y) ^ TryAssassinate(x,y) ¬LoyalTo(x,y)]
TryAssassinate(Marcus, Caesar)
x: Men(x)  People(x)

Prove this
• Given the above sentences, can we make a
conclusion as follows:
“Marcus was not loyal to Caesar ?”
or:
¬loyalto(Marcus,Caesar)
Solution:
In order to prove the goal, we need to use the rules of
inference to transform it into another goal that can in turn be
transformed, and so on, until there are no unsatisfied goals
remaining.

Solution

Unification
• Any substitution that makes two or more expression equal
is called a unifier for the expression.
• Two formulas unify if they can be made identical.
• A unification is a function that assigns bindings to
variables.
• A binding is either a constant, a functional expression or
another variable.

Unification process
• The basic idea of unification is very simple.
• To attempt to unify two literals, we first check if their initial
predicate symbols are the same. If so, we can proceed.
• Otherwise, there is no way they can be unified, regardless of
their arguments. For example, the two literals
tryassassinate (Marcus, Caesar)
hate(Marcus, Caesar)
cannot be unified.
If the predicate symbols match, then we must check the
arguments, one pair at a time. If the first matches, we can
continue with the second, and so on.

Example
P(x, x) P(A,A) {x/A}
P(x, x) P(A,B) fail
P(x, y) P(A,B) {x/A, y/B}
P(x, y) P(A,A) fail
P(x, y) P(A, z) {x/A, y/z}
P(f(x),y)P(f(A),B) {x/A, y/B}
P(x, y) P(f(x),B) fail
P(x, y) P(z, f(z)) {x/z, y/f(z)}

Unification algorithm
• Unify(L1, L2) returns a list representing the
composition of the substitutions that were
performed during the match.
• The empty list, NIL, indicates that a match was
found without any substitutions.
• The list consisting of the single value FAIL
indicates that the unification procedure failed.
• The final substitution produced by the unification
process will be used by the resolution procedure.

Unification algorithm
Algorithm: Unify(L1, L2)
I. If L1 or L2 are both variables or constants, then:
(a) If L1 and L2 are identical, then return NIL.
(b) Else if L1 is a variable, then if L1 occurs in L2 then return {FAIL}, else return
(L2/L1).
(c) Else if L2 is a variable, then if L2 occurs in L1 then return {FAIL} , else return
(L1/L2).
(d) Else return {FAIL}.
2. If the initial predicate symbols in L1 and L2 are not identical, then return {FAIL}.
3. If LI and L2 have a different number of arguments, then return {FAIL}.
4. Set SUBST to NIL. (At the end of this procedure, SUBST will contain all the
substitutions used to unify L1 and L2.)
5. For i ← 1 to number of arguments in L1 :
(a) Call Unify with the ith argument of L1 and the ith argument of L2, putting result
in S.
(b) If S contains FAIL then return {FAIL}.
(c) If S is not equal to NIL then:
(i) Apply S to the remainder of both L1 and L2.
(ii) SUBST: = APPEND(S, SUBST).
6. Return SUBST.

Solve
• (Unification) For each pair of atomic
sentences, give the most general unifier if it
exists, otherwise say “fail”:
a. R(A, x), R(y, z)
b. P(A, B, B), P(x, y, z)
c. Q(y, G(A, B)), Q(G(x,x), y)
d. Older(Father(y), y), Older(Father(x), John)
e. Knows(Father(y),y), Knows(x,x)

Solution
• (Unification) For each pair of atomic
sentences, give the most general unifier if it
exists, otherwise say “fail”:
a. R(A, x), R(y, z) y/A, x/z
b. P(A, B, B), P(x, y, z) x/A, y/B, z/B
c. Q(y, G(A, B)), Q(G(x,x), y) fail
d. Older(Father(y), y), Older(Father(x), John)
x/y, y/John
e. Knows(Father(y),y), Knows(x,x) fail

Resolution Principle
• Given two clauses C1 and C2 with no variables in common, if
there is a literal l1, in C1 and which is a complement of a literal
l2 in C2 , both l1 and l2 are deleted and a disjuncted C is formed
the remaining reduced clauses. The new clauses C is called the
resolve of C1 and C2.
• Resolution is the process of generating these resolvents from
the set of clauses.
Example: (~ PVQ) and (~Q V R)
• We can write
(~ PVQ) , (~Q V R)
~P V R

Refutation
• Resolution produces proofs by refutation.
• In other words, to prove a statement, resolution attempts to
show that the negation of the statement produces a
contradiction with the known statements.

Example of Resolution
• Consider the following clauses:-
A : P V Q V R
B: ~ P V Q V R
C: ~Q V R
 Solution
A: P V Q V R (Given in the problem)
B : ~P V Q V R (Given in the problem)
D : Q V R (Resolvent of A and B)
C: ~Q V R (Given in the problem)
E: R (Resolvent of C and D)

Example of resolution

Example
Assume the following facts:
i. “Steve only likes easy courses.
ii. Science courses are hard.
iii. All the courses in Humanities Department are easy.
iv. HM101 is a course in Humanities”.
Convert the above statements into appropriate wffs so that the
resolution can be performed to answer the question. “ what
course would steve like?”

Solution
First we will convert it into FOPL (First order predicate logic)
i. “Steve only likes easy courses.
x: easy(x) -> likes(steve,x)
ii. Science courses are hard.
x: science(x) -> ~easy(x)
iii. All the courses in Humanities Department are easy.
x: humanities(x) -> easy(x)
iv. HM101 is a course in Humanities”.
humanities(HM101)
The conclusion is encoded as likes(steve , x).

Solution continued….
• First we put our premises in the clause form and the negation
of conclusion to our set of clauses (we use numbers in
parentheses to number the clauses):
(1) ~easy(x)  likes(steve,x)
(2) ~science(x)  ~easy(x)
(3) ~humanities (x)  easy(x)
(4) humanities(HM101)
(5) ~likes(steve,x)

• A resolution proof may be obtained by the following sequence
of resolutions
• (6) 1&5 yields resolvent ~easy(x).
• (7) 3&6 yields resolvent ~humanities (x).
• (8) 4&7 yields empty clause; the substitution x/HM101 is
produced by the unification algorithm which says that the only
wff of the form likes(steve,x) which follows from the premises
is likes(steve, HM101). Thus, resolution gives us a way to find
additional assumptions.

Example
Problem Statement:
1. Ravi likes all kind of food.
2. Apples and chicken are food
3. Anything anyone eats and is not killed is food
4. Ajay eats peanuts and is still alive
5. Rita eats everything that Ajay eats.
Prove by resolution that Ravi likes peanuts using
resolution.

Solution
Step 1: Converting the given statements into
Predicate/Propositional Logic
i. ∀x : food(x) → likes (Ravi, x)
ii. food (Apple) ^ food (chicken)
iii. ∀a : ∀b: eats (a, b) ^ killed (a) → food (b)
iv. eats (Ajay, Peanuts) ^ alive (Ajay)
v. ∀c : eats (Ajay, c) → eats (Rita, c)
vi. ∀d : alive(d) → ~killed (d)
vii. ∀e: ~killed(e) → alive(e)
Conclusion: likes (Ravi, Peanuts)

Solution continued…
Step 2: Convert into CNF
i. ~food(x) v likes (Ravi, x)
ii. Food (apple)
iii. Food (chicken)
iv. ~ eats (a, b) v killed (a) v food (b)
v. Eats (Ajay, Peanuts)
vi. Alive (Ajay)
vii. ~eats (Ajay, c) V eats (Rita, c)
viii. ~alive (d) v ~ killed (d)
ix. Killed (e) v alive (e)
Conclusion: likes (Ravi, Peanuts)Prepared by- Agniwesh Mishra, Rungta

Step 3: Negate the conclusion
~ likes (Ravi, Peanuts)
Step 4: Resolve using a resolution tree

Hence we see that the negation of the conclusion has been proved as a
complete contradiction with the given set of facts. Hence the negation is
completely invalid or false or the assertion is completely valid or true.

Structured Knowledge

Type of Knowledge Structure
• Weak Slot - Filler Structure
– Semantic Nets
– Frame
• Strong Slot - Filler Structure
– Scripts
– Conceptual Dependency

Semantic Nets
• Semantic network or a semantic net is a structure for
representing knowledge as a pattern of interconnected
nodes and arcs.
• It is also representation of knowledge.
• Node in the semantic net represent either
– Entities,
– Attributes,
– State or Events.
• Arcs in the net give the relationship between the nodes.
• Labels on the arc specify what type of relationship actually
exits.

Example: Semantic networks…
• “A sparrow is a bird”
– Two concepts: “sparrow” and “bird”
– sparrow is a kind of bird, so connect the two concepts
with a IS-A relation
This is an higher-lower relation or abstract-concrete
relation
BirdSparrow IS-A

• “A bird has wings”
– This is a different relation: the part-whole relation
– Represented by a HAS-A link or PART-OF link
– The link is from whole to part, so the direction is the
opposite of the IS-A link
Bird
Sparrow IS-A
Wings
HAS-A

Example: Semantic Networks…
• Tweety and Sweety are birds
• Tweety has a red beak
• Sweety is Tweety‟s child
• A crow is a bird
• Birds can fly
• Sparrow is a bird.
• Sparrow has a wing.

BirdSparrow
IS-A
WingsHAS-A
Sweety Tweety
IS-A IS-A
Child-of
Beak
HAS-A
Red Color
Crow IS-A
Fly
Can

Semantic networks can answer
queries
• Query: “Which birds have red beaks?”
• Answer: Tweety
• Method: Direct match of subgraph
• Query: “Can Tweety fly?”
• Answer: Yes
• Method: Following the IS-A link from “Tweety”
to “bird” and the property link of “bird” to “fly”
?
Beak
HAS-A
Red Color

Example: Semantic Networks..
• Scooter is a two wheeler.
• Motor-bike is a two wheeler.
• Motor-bike is a moving-vehicle.
• Moving –vehicle has engine.
• Moving-vehicle has electrical system.
• Moving-vehicle has fuel system.

Example: Semantic Networks…
Scooter
Brakes
Electrical-system
Two-wheeler
Moving-Vehicle
Motor-bike
Engine
Fuel-system
has has
has has
Is_a Is_a

Hierarchical Structure
vehicle
Land-vehicle Water-vehicle Air-vehicle
Road rail river sea aircraft space
Is_a Is_a Is_a
Is_a Is_a

Represent following information in Semantic
net
• (is_a circus-elephant elephant)
• (has elephant head)
• (has elephant trunk)
• (has head mouth)
• (is_a elephant animal)
• (has animal heart)
• (is_a circus-elephant performer)
• (has performer costumes)

Circus-elephant elephant
head trunk
mouth
performer
costumes
animal
heart

Semantic networks
• Advantages of semantic networks
– Simple representation, easy to read
– Associations possible
– Inheritance possible
• Disadvantages of semantic networks
– A separate inference procedure (interpreter) must be build
– The validity of the inferences is not guaranteed
– For large networks the processing is inefficient

Partitioned Semantic Networks
• Hendrix developed the so-called partitioned semantic
network to represent the difference between the description of
an individual object or process and the description of a set of
objects. The set description involves quantification.
• Hendrix partitioned a semantic network whereby a semantic
network, loosely speaking, can be divided into one or more
networks for the description of an individual.

• The central idea of partitioning is to allow groups, nodes and
arcs to be bundled together into units called spaces –
fundamental entities in partitioned networks, on the same level
as nodes and arcs (Hendrix).
• Every node and every arc of a network belongs to (or lies
in/on) one or more spaces.
• Some spaces are used to encode 'background information' or
generic relations; others are used to deal with specifics called
'scratch' space.

• Suppose that we wish to make a specific statement about a
dog, Danny, who has bitten a postman, Peter:
– " Danny the dog bit Peter the postman“
• Hendrix’s Partitioned network would express this statement
as an ordinary semantic network:
Danny
bite
B
postman
Peter
is_a is_a is_a
agent patient
S1
dog

• Suppose that we now want to look at the statement:
– "Every dog has bitten a postman"
• Hendrix partitioned semantic network now comprises two
partitions SA and S1. Node G is an instance of the special
class of general statements about the world comprising link
statement, form, and one universal quantifier

General
Statement dog
D
bite
B
postman
P
is_a is_a is_a
agent patient
S1
G
form

SA
is_a

– "Every dog has bitten every postman"
General
Statement dog
D
bite
B
postman
P
is_a is_a is_a
agent patient
S1
G
form
SA
is_a



– "Every dog in town has bitten the postman"
NB: 'ako' = 'A Kind Of'
General
Statement town dog
D
bite
B
postman
P
is_a is_a is_a
agent patient
S1
G
form
SA
is_a

dog

• The partitioning of a semantic network renders them more
– logically adequate, in that one can distinguish between
individuals and sets of individuals,
– and indirectly more heuristically adequate by way of
controlling the search space by delineating semantic
networks.
• Hendrix's partitioned semantic networks-oriented formalism
has been used in building natural language front-ends for data
bases and for programs to deduct information from databases.

Exercises
• Try to represent the following two sentences into the
appropriate semantic network diagram:
– "John believes that pizza is tasty"
– "Every student loves to party“
– John gave Mary the book

Solution 1: "John believes that pizza is
tasty"
John
believes
event
pizza tasty
object property
agent
is_a
object
has
is_a is_a
space

Solution 2: "Every student loves to party"
GS1
General
Statement
student party love
p1 l1
agent
is_a
is_a
receiver
is_a is_aS2
GS2
s1
S1

is_a
form
exists
form

Solution 3
• John gave Mary the book
Mary
John
Book
Book123
Gave
Event 1Agent Object
Action Instance
Patient

Frame
• Marvin Minsky proposed (1975) frames as a means of
common-sense knowledge.
• Minsky proposed that knowledge is organized into small
packets called frames.
• The contents of the frame are certain slots which have values .
• A Frame can be defined as static data structure that has slots for
various objects and a collection of frames consists of
expectation for a given situation.
• All frames of a given situation constitute the system, whenever
one encounters a situation, a series of related frames are
activated and reasoning is done.

Type of Frame
• Frame is used to represent two type of knowledge
– Declarative/factual/situational/ frame
– Procedural/action frame

Declarative Frame
• A frame that contains only descriptive type of knowledge
called declarative/factual Frame type .

Name : Computer Centre
Air Condition Dumb Terminal
Computer
Stationery
Printer
Slot
Frame Name
Example of Declarative Frame

Procedural Frame
• Apart from the declarative part in a frame, it is possible to attached
slots which explain how to perform things. Or we say that, it is
possible to have procedural knowledge representation in a frame.
The action-frame (Procedural knowledge embedded) has the
following slots-
• Actor Slot- Which holds information about who is performing the
activity.
• Object Slot- This frame has information about the item to be
operated on.
• Source Slot- Source slot holds information from where the action
has to begin
• Destination Slot- Holds information about the place where the
action has to end.
• Task Slot-This generate the necessary sub-frames required to
perform the operation.

Name : Cleaning the jet of carburetor
Expert
Carburetor
Scooter
Remove Carburetor Clean Nozzle Fix Carburetor
Scooter
Source Destination
Object
Actor
Task1 Task2 Task3

Slots can contain these information

Frames: structure

Frames: types
• Slot values can point out another frame.
• By relating frames through slot values a frame system
can be acquired.
Three types of frames can be found in a frame system:
1. Class frame: Such a frame includes slots describing an
attributes of a class of objects. Typically slots of such
frames have default information or unspecified values.

Frames: types

Frames: relationships
• Three types of relationships can relate frames in a frame
system:
1. “Is-a” relationship. Relates a sub-class frame with a class
frame or an instance frame with a sub-class or class frame. In
this case a sub-class frame or an instance frame inherits all
slots from a class frame, but it can include also a new slots.

Frames: relationships

Frames: an example

Frames: facets
• Frames can incorporate facets, which represent extended knowledge
about slot values
• Facets can include:
– Type of a value
– Default value
– Constraints on a value
– Minimum and maximum values
– Actions on values
• They allow controlling of slot values.

Frames: methods
• Methods called demons can be attached to slots.
• Demons are invoked automatically when a slot is
accessed.
Standard demons are the following:
– IF-NEEDED is invoked when it is necessary to acquire
a slot value
– IF-CHANGED is invoked when a value of a slot is
changed
– IF-ADDED is invoked when a value is added to a slot
– IF-REMOVED is invoked when a value of a slot is
deleted

Frames: an example of demons

Frame Structure
• (<frame name>
(<slot1> (<facet1> <value1>....<valuek1>)
» (<facet2> <value1>....<valuek2>)
.
.
.
(<slot2>(<facet1> <value1>....<valuek1>)
.
.
.)

Simple Frame Example
• (bob
(PROFESSION (VALUE professor))
(AGE (VALUE 42))
(WIFE (VALUE sandy))
(CHILDREN (VALUE sue joe))
(ADDRESS (STREET (VALUE wolfstr.))
(CITY (VALUE BONN))
(STATE (VALUE nrw))
(ZIP (VALUE 52100))))

Frame systems
• Frame interpreter
– Each frame system needs an inference mechanism
– Takes care of inheritance, the invoking of demons and the
message passing
• Advantages of frame systems
– The knowledge can be structured
– Flexible inference by using procedural knowledge
– Layered representation and inheritance is possible
• Disadvantages of frame systems
– The design of the interpreter is not easy
– The validity of the inferences is not guaranteed
– Hard to maintain consistency between the knowledge

Conceptual dependency
• It is a theory of how to represent the kind of knowledge about
events that is usually contained in natural language sentences.
• Extension to semantic networks to define a complete set of
primitives to use as relations in semantic networks
• The goal is to represent the knowledge in a way that
– Facilitates drawing inferences from the sentences.
– Is independent of the language in which the sentences
were originally stated.
• The theory was first described by Roger Schank in 1970s.
• Four primitive conceptual categories
– ACT action
– PP object
– AA modifiers of actions
– PA modifiers of objects

Primitive ACTs
• Primitive ACTs represent basic actions
• All actions can be reduced to one or more primitive ACT (with
modifiers)
• 12 primitive ACTs
1. ATRANS transfer a relationship give
2. PTRANS transfer a physical location of an object go
3. PROPEL apply physical force to an object push
4. MOVE move body part by owner kick
5. GRASP grab an object by an actor grasp
6. INGESTingest an object by an animal eat
7. EXPEL expel from an animal‟s body cry
8. MTRANS transfer mental information tell
9. MBUILD mentally make new information decide
10.CONC conceptualize or think about an idea think
11.SPEAK produce sound say
12.ATTEND focus sense organ listenPrepared by- Agniwesh Mishra, Rungta

Explanation
• Rule 1: describes the relationship between an actor and the event he
or she causes. The letter p above the dependency indicates past
tense.
• Rule 2: describes the relationship between a PP and a PA.
• Rule 3: describes the relationship between two PPs, one of which
belongs to the set defined by the other.
• Rule 4: describes the relationship between a PP and an attribute that
has already been predicated of it.
• Rule 5: describes the relationship between two PPs, one of which
provides a particular kind of information about the other.
• Rule 6: describes the relationship between an ACT and PP that is
the object of that ACT.
• Rule 7: describes the relationship between an ACT and the source
and , the recipient of the ACT.

Explanation
• Rule 8: describes the relationship between an ACT and the
instrument with which it is performed.
• Rule 9: describes the relationship between an ACT and its physical
source and destination.
• Rule 10: represents the relationship between a PP and a state in
which it started and another in which it ended.
• Rule 11: describes the relationship between one conceptualization
and another that causes it.
• Rule 12: describes the relationship between a conceptualization and
the time at which the event it describes occurred.
• Rule 13: describes the relationship between one conceptualization
and another that is the time of the first.
• Rule 14: describes the relationship between a conceptualization and
the place at which it occurred.

Primitive conceptual tenses
1. p Past
2. f Future
3. t Transition
4. k Continuing
5. ts Start transition
6. tk Finish transition
7. ? Interrogative
8. / Negative
9. Nil Present
10.Delta Timeless
11.C Condition

Example

Example
“Since, smoking can kill one, I stooped”.

Example
Bill threatened John with a broken nose.

Conceptual Dependency
Advantages:
– Fewer inference rules are needed.
– Many inferences are already contained in the
representation.
– Holes in the representation can serve as an attention
focuser.
Disadvantages:
Requires knowledge to be decomposed into fairly low-
level primitives is only a theory of the representation of
events.
can't produce them automatically form NL
– needs to be built by hand

Solve
1. Write a conceptual dependency structure for the
following:
– John begged Marry for a pencil. (rule 7)
– While going I saw snake. (rule 13)
2. Formulate CD structure for the following sentence:”
“Since, smoking can kill one, I stooped”. (slide 152)
3. Express the following sentence as CD structure
– Sam gave Mary a box of candy. (rule 7)
– Bill and Ram is a programmer. (rule 3)
– Charlie drive a car but not bike.

Scripts
• Developed by Roger Schank, late 1970s
• We need large amounts of background knowledge to
understand even the simplest conversation
– “Sue went out to lunch. She sat at a table and called a
waitress, who brought her a menu. She ordered a
sandwich.”
– questions:
why did the waitress bring a menu to Sue?
who was the “she” who ordered a sandwich?
who paid?
• Claim: people organize background knowledge into
structures that correspond to typical situations (scripts)
• Script: A typical scenario of what happens in…
– a restaurant
– a soccer game
– a classroom

Scripts
• A script is a knowledge representation structure that is
extensively used for describing stereo type sequences of
action.
• It is special case of frame structure.
• It represent events that takes place in day – to – day activities.
• Script do have slots and with each slots, we associate info
about the slot.

Components of scripts
1. Entry conditions
– Preconditions:
 facts that must be true to call the script
– Eg.: an open restaurant, a hungry customer that has some
money
2. Results
– Postconditions:
 facts that will be true after the script has terminated
– Eg.: customer is full and has less money; restaurant owner
has more money

Components of scripts
3. Props
– Typical things that support the content of the script
– Eg.: waiters, tables, menus
4. Roles
– Actions that participants perform
– Represented using conceptual dependency
– Eg.: waiter takes orders, delivers food, presents bill
5. Scenes
– A temporal aspect of the script
– Eg.: entering the restaurant, ordering, eating, …
6. Track
– represents a specific instance of a generic pattern.
– Restaurant is a specific instance of a hotel. This slot
permits one to inherit the characteristics of the generic
node. Prepared by- Agniwesh Mishra, Rungta

Food Market Example
i. SCRIPT-NAME : food market
ii. TRACK : supermarket
iii. PROPS : shoping cart
market items
checkout statnds
cashier
money
iv. ROLES : shopper
daily attendant
food attendant
checkout clerk
other shoppers
v. ENTRY
CONDITION : shopper needs food market open
vi. RESULTS:

Food Market Example...
• Scene1 : Enter Market
Shopper PTRANS Shopper into market
Shopper PTRANS Shopping –cart to shopper
• Scene2 : Shop for Items
Shopper MOVE shopper through aisles
Shopper ATTEND eyes to display items
Shopper PTRANS items to shopping cart
• Scene3 :Check out
Shopper MOVE shopper to checkout stand
Shopper WAIT shopper turn
Shopper ATTEND eyes to charges
Shopper ATRANS money to cashier
Sacker ATRANS bags to shopper
• Scene4 : Exit Market
Shopper PTRANS shopper to exit market

Food Market Example...
• Results : Shopper has less money
Shopper has grocery items
Market has less grocery items
Market has more money

Example of a script

Agents
• An agent is anything that can be viewed as perceiving
its environment through sensors and acting upon that
environment through actuators.
• A human agent has eyes, ears and other organs for
sensors and hands, legs, mouth and other body parts
for actuators.
• A robotics agent might have cameras and infrared
range finders for sensors and various motors for
actuators.
• A software agent receives keystrokes, file contents ,
and network packets as sensory inputs and acts on the
environment by displaying on the screen , writing
files, and sending network packets.Prepared by- Agniwesh Mishra, Rungta

Agents

Structure of Agents
• Agent = architecture + program
• architecture
– device with sensors and actuators
– e.g., A robotic car, a camera, a PC, …
• program
– implements the agent function on the architecture

Types of Agents
• Reflex Agent
• Reflex Agent with State
• Goal-based Agent
• Utility-Based Agent
• Learning Agent

Reflex Agent

Reflex Agent with State

State Management
• Reflex agent with state
– Incorporates a model of the world
– Current state of its world depends on percept
history
– Rule to be applied next depends on resulting
state
• state’  next-state( state, percept )
action  select-action( state’, rules )

Goal-based Agent

Incorporating Goals
• Rules and “foresight”
– Essentially, the agent’s rule set is determined
by its goals
– Requires knowledge of future consequences
given possible actions
• Can also be viewed as an agent with more
complex state management
– Goals provide for a more sophisticated
next-state function

Utility-based Agent

Incorporating Performance
• May have multiple action sequences that
arrive at a goal
• Choose action that provides the best level
of “happiness” for the agent
• Utility function maps states to a measure
– May include tradeoffs
– May incorporate likelihood measures

Learning Agent

Incorporating Learning
• Can be applied to any of the previous agent
types
– Agent <-> Performance Element
• Learning Element
– Causes improvements on agent/ performance
element
– Uses feedback from critic
– Provides goals to problem generator

Knowledge-based agents
• Knowledge-based agents can benefit from knowledge expressed in
very general forms, combining and recombining information to suit
myriad purposes.
• Often, this process can be quite far removed from the needs of the
moment—as when a mathematician proves a theorem or an
astronomer calculates the earth’s life expectancy.
• A knowledge-based agent can combine general knowledge with
current percepts to infer hidden aspects of the current state prior to
selecting actions.
• For example, a physician diagnoses a patient—that is, infers a
disease state that is not directly observable—prior to choosing a
treatment.
• Some of the knowledge that the physician uses in the form of rules
learned from textbooks and teachers, and some is in the form of
patterns of association that the physician may not be able to
consciously describe. If its inside the physician’s head, it counts as
knowledge.

Knowledge-based agents continue….
• Understanding natural language also requires inferring
hidden state, namely, the intention of the speaker.
• When we hear, “John saw the diamond through the
window and coveted it,” we know “it” refers to the
diamond and not the window—we reason, perhaps
unconsciously, with our knowledge of relative value.
• Similarly, when we hear, “John threw the brick through
the window and broke it,” we know “it” refers to the
window.
• Reasoning allows us to cope with the virtually infinite
variety of utterances using a finite store of
commonsense knowledge.

• Problem-solving agents have difficulty with this
kind of ambiguity because their representation of
contingency problems is inherently exponential.
• Our final reason for studying knowledge-based
agents is their flexibility.
• They are able to accept new tasks in the form of
explicitly described goals, they can achieve
competence quickly by being told or learning new
knowledge about the environment, and they can
adapt to changes in the environment by updating
the relevant knowledge.

• The central component of a knowledge-based agent is its knowledge
base, or KB.
• Informally, a knowledge base is a set of sentences. (Here “sentence” is
used as a technical term.
• It is related but is not identical to the sentences of English and other
natural languages.) Each sentence is expressed in a language called a
knowledge representation language and represents some assertion
about the world.
• There must be a way to add new sentences to the knowledge base and a
way to query what is known.
• The standard names for these tasks are TELL and ASK, respectively. Both
tasks may involve inference—that is, deriving new sentences from old. In
logical agents, which are the main subject of study in this chapter,
inference must obey the fundamental requirement that when one ASKs a
question of the knowledge base, the answer should follow from what has
been told (or rather, TELLed) to the knowledge base previously.
• Later in the chapter, we will be more precise about the crucial word
“follow.” For now, take it to mean that the inference process should not
just make things up as it goes along.

A generic knowledge-based agent

• Above figure shows the outline of a knowledge-based agent
program. Like all our agents, it takes a percept as input and returns
an action.
• The agent maintains a knowledge base, KB, BACKGROUND which
may initially contain some background knowledge. Each time the
agent program is KNOWLEDGE called, it does two things. First, it
TELLs the knowledge base what it perceives. Second, it ASKs the
knowledge base what action it should perform.
• In the process of answering this query, extensive reasoning may be
done about the current state of the world, about the outcomes of
possible action sequences, and so on.
• Once the action is chosen, the agent records its choice with TELL
and executes the action. The second TELL is necessary to let the
knowledge base know that the hypothetical action has actually
been executed.

• The knowledge-based agent is not an arbitrary program for
calculating actions. It is amenable to a description at the
KNOWLEDGE LEVEL knowledge level, where we need specify only
what the agent knows and what its goals are, in order to fix its
behavior.
• For example, an automated taxi might have the goal of delivering a
passenger to Marin County and might know that it is in San
Francisco and that the Golden Gate Bridge is the only link between
the two locations. Then we can expect it to cross the Golden Gate
Bridge because it knows that that will achieve its goal. Notice that
this analysis IMPLEMENTATION is independent of how the taxi
works at the implementation level.
• It doesn’t matter whether LEVEL its geographical knowledge is
implemented as linked lists or pixel maps, or whether it reasons by
manipulating strings of symbols stored in registers or by
propagating noisy signals in a network of neurons.

• One can build a knowledge-based agent simply by TELLing it what it needs
to know. The agent’s initial program, before it starts to receive percepts, is
built by adding one by one the sentences that represent the designer’s
knowledge of the environment.
• Designing the representation language to make it easy to express this
knowledge in the form of sentences simplifies the construction problem
DECLARATIVE enormously.
• This is called the declarative approach to system building.
• In contrast, the procedural approach encodes desired behaviors directly
as program code; minimizing the role of explicit representation and
reasoning can result in a much more efficient system.
• We will see agents of both kinds in Section 7.7. In the 1970s and 1980s,
advocates of the two approaches engaged in heated debates. We now
understand that a successful agent must combine both declarative and
procedural elements in its design.

• In addition to TELLing it what it needs to know, we can
provide a knowledge-based agent with mechanisms that
allow it to learn for itself.
• These mechanisms, create general knowledge about the
environment out of a series of percepts.
• This knowledge can be incorporated into the agent’s
knowledge base and used for decision making. In this way,
the agent can be fully autonomous.
• All these capabilities—representation, reasoning, and
learning—rest on the centuries long development of the
theory and technology of logic.
• Before explaining that theory and technology, however, we
will create a simple world with which to illustrate them.

Boolean circuit agents

Artificial Intelligence Notes Unit 2

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Artificial Intelligence Notes Unit 2