Artificial intelligence knowledge representation and learning

Artificial
Intelligence
Knowledge Representation and
Learning
28-11-2022 KNOWLEDGE REPRESENTATION - AI 1

Contents
Knowledge-based agents
Wumpus world
Logic in general - models and entailment
Propositional (Boolean) logic
Equivalence, validity, satisfiability
Inference rules and theorem proving
▪ Forward chaining
▪ Backward chaining
▪ Resolution

Logical Agents
Humans can know “things” and “reason”
Representation: How are things stored?
Reasoning: How is the knowledge used?
• To solve a problem…
• To generate more knowledge…
• Knowledge and reasoning are important to artificial agents because they
enable successful behaviors difficult to achieve otherwise
– Useful in partially observable environments

“Logical AI:
The idea is that an agent can represent knowledge of its world,
its goals and the current situation by sentences in logic and decide
what to do by inferring that a certain action or course of action is
appropriate to achieve its goals.”
Logical Agents

Knowledge Base

Knowledge-Based Agents
Central component of a Knowledge-Based Agent is a Knowledge-Base
A set of sentences in a formal language
Sentences are expressed using a knowledge representation language
Two generic functions:
– TELL - add new sentences (facts) to the KB • “Tell it what it needs
to know”
– ASK - query what is known from the KB • “Ask what to do next”

A simple knowledge-based agent

The Wumpus World environment
The Wumpus computer game
The agent explores a cave consisting of rooms
connected by passageways
Lurking somewhere in the cave is the Wumpus, a beast
that eats any agent that enters its room
Some rooms contain bottomless pits that trap any agent
that wanders into the room
The Wumpus can fall into a pit too, so avoids them
Occasionally, there is a heap of gold in a room.
The goal is to collect the gold and exit the world without
being eaten

AIMA’s Wumpus World
The agent always starts in the field
[1,1]
Agent’s task is to find the gold, return
to the field [1,1] and climb out of the
cave

The Hunter’s first step
¬W
¬W
Since the agent is alive and perceives neither
a breeze nor a stench at [1,1], it knows that
[1,1] and its neighbors are OK
Moving to [2,1] is a safe move that reveals a
breeze but no stench, implying that the Wumpus
is not adjacent but that one or more pits are

Exploring a wumpus world
A agent
B breeze
G glitter
OK safe cell
P pit
S stench
W wumpus

A agent
B breeze
G glitter
OK safe cell
P pit
S stench
W wumpus

A agent
B breeze
G glitter
OK safe cell
P pit
S stench
W wumpus
P?
P?
In each case where the agent draws a conclusion from the available
Information, that conclusion is guaranteed to be correct if the available
Information is correct…
This is a fundamental property of logical reasoning

Performance Measure: Performance measure is the unit to define the success
of an agent. Performance varies with agents based on their different precepts.
Environment: Environment is the surrounding of an agent at every instant. It
keeps changing with time if the agent is set in motion.
Actuator: An actuator is a part of the agent that delivers the output of action to
the environment.
Sensor: Sensors are the receptive parts of an agent that takes in the input for
the agent.

Logic as a KR language
Propositional Logic
First Order
Higher Order
Modal
Fuzzy
Logic
Multi-valued
Logic
Probabilistic
Logic
Temporal Non-monotonic
Logic

Logics
Logics are formal languages for representing information such that conclusions
can be drawn
Syntax defines the sentences in the language
Semantics define the "meaning" of sentences; define truth of a sentence in a
world
E.g., the language of arithmetic
❖ x+2 ≥ y is a sentence
❖ x2+y > {} is not a sentence
❖ x+2 ≥ y is true iff the number x+2 is no less than the number y
❖ x+2 ≥ y is true in a world where x = 7, y = 1

Inference and Entailment
Inference is a procedure that allows new sentences to be derived from a
knowledge base.
Understanding inference and entailment: think of
Set of all consequences of a KB as a haystack
α as the needle
Entailment is like the needle being in the haystack
Inference is like finding it

Inference and Entailment

Propositional Logic
Propositional logic (PL) is the simplest logic.
• Syntax of PL: defines the allowable sentences or propositions.
• Definition (Proposition): A proposition is a declarative statement (True or False).
A fact, like “the Sun is hot.” The Sun cannot be both hot and not hot at the
same time. This declarative statement could also be referred to as a proposition.
• Atomic proposition: single proposition symbol. Each symbol is a proposition.
Notation: upper case letters and may contain subscripts.
• Compound proposition: constructed from from atomic propositions using parentheses and
logical connectives.

Atomic Proposition
Examples of atomic propositions:
2+2=4 is a true proposition
W1,3 is a proposition. It is true if there is a Wumpus in [1,3]
“If there is a stench in [1,2] then there is a Wumpus in [1,3]”
is a proposition
“How are you?” or “Hello!” are not propositions. In general,
statement that are questions, commands, or opinions are not
propositions.

Examples of compound/complex propositions
Let p, p1, and p2 be propositions
• Negation ¬p is also a proposition. A literal is either an atomic proposition or
its negation. E.g., W1,3 is a positive literal, and ¬W1,3 is a negative literal.
• Conjunction p1 ∧ p2. E.g., W1,3 ∧ P3,1
• Disjunction p1 ∨ p2 E.g., W1,3 ∨ P3,1
• Implication p1 → p2. E.g., W1,3 ∧ P3,1 → ¬W2,2
• If and only if p1 ↔ p2. E.g., W1,3 ↔ ¬W2,2

Truth Table
The semantics define the rules to determine the truth of a sentence.
• Semantics can be specified by truth tables.
• Boolean values domain: T,F , n-tuple: (x1, x2, ..., xn)
• Operator on n-tuples : g(x1 = v1, x2 = v2, ..., xn = vn)
• A truth table defines an operator g on n- tuples by specifying a boolean value for each
tuple.
• Number of rows in a truth table? R = 2n

Building Propositions
Negation Conjunction
Disjunction Implication
If and only if
Exclusive OR

Logical Equivalence
Two propositions p and q are logically equivalent if and only if the columns in
the truth table giving their truth values agree.
• We write this as p ⇔ q or p ≡ q.

Tautology and contradiction
• Tautology is a proposition which is always true
• Contradiction is a proposition which is always false
• Contingency is a proposition which is neither a tautology or a contradiction

Contrapositive and Inverse
Given an implication p → q
• The converse is: q → p
• The contrapositive is: ¬q →
¬p
• The inverse is: ¬p → ¬q
Conditional statement: “If it is raining, then the grass is wet.”
The first step is to identify the hypothesis and conclusion
statements. Hypothesis, p: it is raining
Conclusion, q: grass is wet
Converse statement would be: “If the grass is wet, then it is
raining.”
Inverse statement would be: “If it is NOT raining, then the grass is
NOT wet.”
Contrapositive statement would be: “If the grass is NOT wet, then it
is NOT raining.”

Inference (Modus Ponens)

Modus Tollens

And Elimination, Unit Resolution

Wumpus world – Knowledge Base
Atomic propositions

Wumpus world – Knowledge Base

Propositional Rules
Room[1,1], room does not have
wumpus(¬ W11), no stench (¬S11), no
Pit(¬P11), no breeze(¬B11), no gold
(¬G11), visited (V11), and the room is
Safe(OK11).

Inference
Prove that wumpus is in the room (1, 3) using propositional rules which have
been derived for the wumpus world and using inference rule.
•Apply Modus Ponens with ¬S11 and R1:

Inference
Apply And-Elimination Rule:
After applying And-elimination rule to ¬ W11 ∧ ¬ W12 ∧ ¬ W21, we will get
three statements:
¬ W11, ¬ W12, and ¬W21.
•Apply Modus Ponens to ¬S21
, and R2:
Now we will apply Modus Ponens to ¬S21
and R2 which is ¬S21
→ ¬ W21
∧¬
W22
∧ ¬ W31
, which will give the Output as ¬ W21
∧ ¬ W22
∧¬ W31

Inference
•Apply And -Elimination rule:
Now again apply And-elimination rule to ¬ W21
∧ ¬ W22
∧¬ W31
, We will get
three statements:
¬ W21
, ¬ W22
, and ¬ W31
.
•Apply MP to S12
and R4:
Apply Modus Ponens to S12
and R4
which is S12
→ W13
∨. W12
∨. W22
∨.W11
,
we will get the output as W13
∨ W12
∨ W22
∨.W11
.

Inference
•Apply Unit resolution on W13
∨ W12
∨ W22
∨W11
and ¬ W11
:
After applying Unit resolution formula on W13
∨ W12
∨ W22
∨W11
and ¬
W11
we will get W13
∨ W12
∨ W22
.

Inference
•Apply Unit resolution on W13
∨ W12
∨ W22
and ¬ W22
:
After applying Unit resolution on W13
∨ W12
∨ W22
, and ¬W22
, we will get W13
∨ W12
as
output.

Inference
•Apply Unit Resolution on W13
∨ W12
and ¬ W12
:
After Applying Unit resolution on W13
∨ W12
and ¬ W12
, we will get W13
as an output, hence it
is proved that the Wumpus is in the room [1, 3].

Demerit of Propositional Logic
Propositional logic can only represent the facts, which are either true or false.
PL is not sufficient to represent the complex sentences or natural language
statements.
The propositional logic has very limited expressive power.
Consider the following sentence, which we cannot represent using PL logic.
•"Some humans are intelligent", or
•"Sachin likes cricket."

First-Order Logic (FOL)
It is an extension to propositional logic.
FOL is sufficiently expressive to represent the natural language statements in a
concise way.
First-order logic is also known as Predicate logic or First-order predicate logic.

First-order Logic
First-order logic (FOL) models the world in
terms of
◦Objects, which are things with individual
identities
◦Properties of objects that distinguish them
from other objects
◦Relations that hold among sets of objects
◦Functions, which are a subset of relations
where there is only one “value” for any
given “input”
Examples:
Objects: Students, lectures…
Relations: Brother-of,
bigger-than, outside..
Properties: blue, oval, even,
large, ...
Functions: father-of,
best-friend, second-half,
one-more-than ...

Basic Element in FOL

Atomic Sentences in FOL
Atomic sentences are the most basic sentences of first-order logic. These
sentences are formed from a predicate symbol followed by a parenthesis with a
sequence of terms.
Atomic sentences are represented as Predicate (term1, term2, ......, term n).
Example: Ravi and Ajay are brothers: => Brothers(Ravi, Ajay).
Chinky is a cat: => cat (Chinky).
Complex Sentences:
Complex sentences are made by combining atomic sentences using
connectives.

Parts of FOL
First-order logic statements can be divided into two
parts:
•Subject: Subject is the main part of the statement.
•Predicate: A predicate can be defined as a relation,
which binds two atoms together in a statement.
Consider the statement: "x is an integer.", it consists
of two parts, the first part x is the subject of the
statement and second part "is an integer," is known as
a predicate.

Quantifiers in First-order logic
A quantifier is a language element which generates quantification, and
quantification specifies the quantity of specimen in the universe of discourse.
These are the symbols that permit to determine or identify the range and scope
of the variable in the logical expression. There are two types of quantifier:
Universal Quantifier, (for all, everyone, everything)
Existential quantifier, (for some, at least one).

Universal Quantifier
Universal quantifier is a symbol of logical representation, which specifies that the statement
within its range is true for everything or every instance of a particular thing.
The Universal quantifier is represented by a symbol ∀, which resembles an inverted A.
If x is a variable, then ∀x is read as:
•For all x
•For each x
•For every x.

Universal Quantifier
Example: All man drink coffee.
Let a variable x which refers to a man so all x can be represented in UOD as below:
It will be read as: There are all x where x is a
man who drink coffee.

Existential Quantifier
Existential quantifiers are the type of quantifiers, which express that the
statement within its scope is true for at least one instance of something.
It is denoted by the logical operator ∃, which resembles as inverted E.
When it is used with a predicate variable then it is called as an existential
quantifier.
If x is a variable, then existential quantifier will be ∃x or ∃(x). And it will be
read as:

Existential Quantifier
It will be read as: There are some x where x is a
boy who is intelligent.

FOL
Points to remember:
The main connective for universal quantifier ∀ is implication →.
The main connective for existential quantifier ∃ is and ∧.
Properties of Quantifiers:
In universal quantifier, ∀x∀y is similar to ∀y∀x.
In Existential quantifier, ∃x∃y is similar to ∃y∃x.
∃x∀y is not similar to ∀y∃x.

Examples of FOL
1. All birds fly.
In this question the predicate is "fly(bird)."
And since there are all birds who fly so it will be represented as follows.
∀x bird(x) →fly(x).
2. Every man respects his parent.
In this question, the predicate is "respect(x, y)," where x=man, and y= parent.
Since there is every man so will use ∀, and it will be represented as follows:
∀x man(x) → respects (x, parent).
3. Some boys play cricket.
In this question, the predicate is "play(x, y)," where x= boys, and y= game. Since
there are some boys so we will use ∃, and it will be represented as:
∃x boys(x) → play(x, cricket).

Examples of FOL
4. Not all students like both Mathematics and Science.
In this question, the predicate is "like(x, y)," where x= student, and y= subject.
Since there are not all students, so we will use ∀ with negation, so following
representation for this:
¬∀ (x) [ student(x) → like(x, Mathematics) ∧ like(x, Science)].
5. Only one student failed in Mathematics.
In this question, the predicate is "failed(x, y)," where x= student, and y=
subject.
Since there is only one student who failed in Mathematics, so we will use
following representation for this:
∃(x) [ student(x) → failed (x, Mathematics) ∧∀ (y) [¬(x==y) ∧
student(y) → ¬failed (x, Mathematics)].

Free and Bound Variable
There are two types of variables in First-order logic which are given below:
Free Variable: A variable is said to be a free variable in a formula if it occurs outside
the scope of the quantifier.
Example: ∀x ∃(y)[P (x, y, z)], where z is a free variable.
Bound Variable: A variable is said to be a bound variable in a formula if it occurs within
the scope of the quantifier.
Example: ∀x [A (x) B( y)], here x and y are the bound variables.

Unification
The process of finding a substitution for predicate parameters is called
unification.
We need to know:
◦that 2 literals can be matched.
◦the substitution is that makes the literals identical.
There is a simple algorithm called the unification algorithm that does this.

The Unification Algorithm

Unification Example
Let Ψ1
= King(x), Ψ2
= King(John),
Substitution θ = {John/x} is a unifier for these
atoms and applying this substitution, and both
expressions will be identical.

Unification Example

Conditions for Unification:
Following are some basic conditions for unification:
•Predicate symbol must be same, atoms or expression with different predicate
symbol can never be unified.
•Number of Arguments in both expressions must be identical.
•Unification will fail if there are two similar variables present in the same
expression.

Resolution
Resolution is a single inference rule which can efficiently operate on
the conjunctive normal form or clausal form.
Example

Steps for resolution

Resolution- Example

Step-3: Negate the statement to be
proved
In this statement, we will apply negation to
the conclusion statements, which will be
written as ¬likes(John, Peanuts)

Inference engine
The inference engine is the component of the intelligent system in artificial intelligence, which
applies logical rules to the knowledge base to infer new information from known facts.
Inference engine commonly proceeds in two modes, which are:
Forward chaining
Backward chaining

Forward Chaining
Forward chaining is a form of reasoning which start with atomic
sentences in the knowledge base and applies inference rules (Modus
Ponens) in the forward direction to extract more data until a goal is
reached.
The Forward-chaining algorithm starts from known facts, triggers all
rules whose premises are satisfied, and add their conclusion to the
known facts. This process repeats until the problem is solved.

Forward Chaining
Given a new fact, generate all consequences
Assumes all rules are of the form
◦C1 and C2 and C3 and…. --> Result
Each rule & binding generates a new fact
This new fact will “trigger” other rules
Keep going until the desired fact is generated
(Semi-decidable as is FOL in general)

FC: Example Knowledge Base
The law says that it is a crime for an American to sell weapons to hostile nations. The country
Nono, an enemy America, has some missiles, and all of its missiles were sold to it by Col. West,
who is an American.
Prove that Col. West is a criminal.

…it is a crime for an American to sell weapons to hostile nations
American(x) ∧Weapon(y) ∧Sells(x,y,z) ∧Hostile(z) ⇒ Criminal(x)
Nono…has some missiles
∃x Owns(Nono, x) ∧ Missiles(x)
Owns(Nono, M1
) and Missle(M1
)
…all of its missiles were sold to it by Col. West
∀x Missle(x) ∧ Owns(Nono, x) ⇒ Sells( West, x, Nono)
Missiles are weapons
Missle(x) ⇒ Weapon(x)

An enemy of America counts as “hostile”
Enemy( x, America ) ⇒ Hostile(x)
Col. West who is an American
American( Col. West )
The country Nono, an enemy of America
Enemy(Nono, America)

Backward Chaining
Consider the item to be proven a goal
Find a rule whose head is the goal (and bindings)
Apply bindings to the body, and prove these (subgoals) in turn
If you prove all the subgoals, increasing the binding set as you go,
you will prove the item.

Backward Chaining Example

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