Knowledge representation:
A variety of ways of knowledge (facts) have been exploited in AI programs. Facts: truths in some relevant world. These are things we want to represent.

**LOGICAL AGENTS**

**Knowledge representation**

A variety
of ways of knowledge (facts) have been exploited in AI programs. Facts: truths
in some relevant world. These are things we want to represent.

**Propositional logic**

It is a
way of representing knowledge.In logic and mathematics, a **propositional calculus** or **logic**
is a formal system in which formulae representing *propositions* can be formed by combining atomic propositions

using *logical connectives*

Sentences
considered in propositional logic are not arbitrary sentences but are the ones
that are either true or false, but not both. This kind of sentences are called **propositions**.

**Example**

Some
facts in propositional logic:

It is
raining. - RAINING

It is
sunny - SUNNY

It is
windy - WINDY

If it is
raining ,then it is not sunny - RAINING -> SUNNY

**Elements of propositional logic**

Simple
sentences which are true or false are basic propositions. Larger and more
complex sentences are constructed from basic propositions by combining them
with **connectives**. Thus **propositions** and **connectives** are the basic elements of propositional logic. Though
there are many connectives, we are going to use the following **five basic connectives** here: NOT, AND,
OR, IF_THEN (or IMPLY), IF_AND_ONLY_IF. They are also denoted by the symbols: ,
, , , , respectively.

**Inference**

Inference
is deriving new sentences from old.

**Modus ponens**

There are
standard patterns of inference that can be applied to derive chains of
conclusions that lead to the desired goal. These patterns of inference are
called **inference**

**rules.**

**Entailment**

Propositions
tell about the notion of truth and it can be applied to logical reasoning. We
can have logical entailment between sentences. This is known as entailment
where a sentence follows logically from another sentence.In mathematical
notation we write : **knowledge** based
agents or logical agents.The central component of a knowledge-based agent is
its knowledge base, or KB.

Informally,a
knowledge base is a set of sentences. Each sentence is expressed in language
called a knowledge representation language and represents some assertion about
the world.

The **syntax** of propositional logic defines
the allowable sentences. The **atomic
sentences-**

the
indivisible syntactic elements-consist of a single **proposition symbol.** Each such symbol tands for a proposition that
can be true or false. We will use uppercase names for symbols: P, Q, R, and so
on.

**Complex sentences **are
constructed from simpler sentences using**
logical connectives. **There are five connectives in common use:

**First order Logic**

Whereas
propositional logic assumes the world contains facts, first-order logic (like
natural language) assumes the world contains

**Objects: **people, houses, numbers, colors,
baseball games, wars, …

**Relations: **red, round, prime, brother of,
bigger than, part of, comes between,**
Functions: **father of, best friend, one more than, plus,

The basic
syntactic elements of -orderlogicare. the
symbols that stand for objects, relations, and functions. The symbols,come in
three kinds:

a) constant
symbols, which stand for objects;

b) predicate
symbols, which stand for relations;

c) and
function symbols, which stand for functions.

We adopt
the convention that these symbols will begin with uppercase letters.

Example:
Constant symbols : Richard

and John;
predicate symbols :

Brother,
OnHead, Person, King, and

Crown;
function symbol :LeftLeg.

**Quantifiers**

There is
need to express properties of entire collections of objects,instead of
enumerating the objects by name. Quantifiers let us do this.FOL contains two
standard quantifiers called

a) Universal
( **)**
and

b) Existential
( )

**Universal quantification**

( x) P(x)
: means that P holds for**all** values
of x in the domain associated with that variable

E.g., (
x) dolphin(x) => mammal(x)

**Existential quantification**

( x)P(x) means that P holds for **some** value of x in the domain
associated with that

variable

E.g., (
x) mammal(x) ^ lays-eggs(x)

Permits
one to make a statement about some object without naming it

**Explain Universal Quantifiers with an example.**

Rules
such as "All kings are persons,'' is written in first-order logic as

x King(x)
=> Person(x)

where is
pronounced as “ For all ..”

Thus, the
sentence says, "For all *x,* if *x* is a king, then z is a person."
The symbol x is called a variable(lower case letters)

The
sentence **x** P,where P is a logical
expression says that P is true for every object x.

**Existential quantifiers with an example.**

Universal
quantification makes statements about every object. It is possible to make a
statement about some object in the universe without naming it,by using an
existential quantifier.

Example

“King
John has a crown on his head” x Crown(x) ^ OnHead(x,John)

x is
pronounced There“ exists an x such that ..” or “ For some x ..” **connection between **universal** and **existential quantifiers

“Everyone
likes icecream “ is equivalent “there is no one who does not like icecream”

This can
be expressed as :

x
Likes(x,IceCream) isquivalent to Likes(x,IceCream)

**STEPS ASSOCIATED WITH THE KNOWLEDGE ENGINEERING PROCESS**

**Knowledge Engineering**

Discuss
them by applying the steps to any real world application of your choice. The
general process of knowledge base construction a process is called knowledge
engineering. A knowledge engineer is someone who investigates a particular
domain, learns what concepts are important in that domain, and creates a formal
representation of the objects and relations in the domain. We will illustrate
the knowledge engineering process in an electronic circuit domain that should
already be fairly familiar,

**The steps associated with the knowledge engineering
process are :**

1. *Identfy
the task**.*

. The
task will determine what knowledge must be represented in order to connect
problem instances to answers. This step is analogous to the PEAS process for
designing agents.

*2. **Assemble
the relevant knowledge**. *The knowledge engineer might
already be an

*3. **Decide on
a vocabulary of predicates, functions, and constants**. *That is,
translate

Once the
choices have been made. the result is a vocabulary that is known as the **ontology** of the domain. The word *ontology* means a particular theory of
the nature of being or existence.

*4. Encode general /knowledge about the domain.*

The
knowledge engineer writes down the axioms for all the vocabulary terms. This
pins down (to the extent possible) the meaning of the terms, enabling the
expert to check the content. Often, this step reveals misconceptions or gaps in
the vocabulary that must be fixed by returning to step 3 and iterating through
the process.

*5. Encode a description of the specific problem
instance.*

For a
logical agent, problem instances are supplied by the sensors, whereas a
"disembodied" knowledge base is supplied with additional sentences in
the same way that traditional programs are supplied with input data.

6. *Pose queries to the inference procedure and
get answers.*

This is
where the reward is: we can let the inference procedure operate on the axioms
and problem-specific facts to derive the facts we are interested in knowing.

*7. Debug the knowledge base.*

*x **NumOfLegs(x,4) => Mammal(x) Is false for reptiles ,amphibians.*

To
understand this seven-step process better, we now apply it to an extended
example-the domain of electronic circuits.

**The electronic circuits domain**

We will
develop an ontology and knowledge base that allow us to reason about digital
Circuits of the kind shown in Figure 8.4. We follow the seven-step process for
knowledge engineering There are many reasoning tasks associated with digital
circuits. At the highest level, one analyzes the circuit's functionality. For
example, what are all the gates connected to the first input terminal? Does the
circuit contain feedback loops? These will be our tasks in this section.

**Assemble the relevant knowledge**

What do
we know about digital circuits? For our purposes, they are composed of wires
and gates. Signals flow along wires to the input terminals of gates, and each
gate produces a decide

on
vocabulary.

We now
know that we want to talk about circuits, terminals, signals, and gates. The
next step is to choose functions, predicates, and constants to represent them.
We will start from individual gates and move up to circuits. First, we need to
be able to distinguish a gate from other gates. This is handled by naming gates
with constants: X I , X2, and so on

**Encode general knowledge of the domain**

One sign
that we have a good ontology is that there are very few general rules which
need to be specified. A sign that we have a good vocabulary is that each rule
can be stated clearly and concisely. With our example, we need only seven
simple rules to describe everything we need to know about circuits:

1. If two
terminals are connected, then they have the same signal:

2. The
signal at every terminal is either 1 or 0 (but not both):

3. Connected
is a commutative predicate:

4. An OR
gate's output is 1 if and only if any of its inputs is 1:

*5. *An **A.ND** gate's output is 0 if and only if
any of its inputs is 0:

6. An XOR
gate's output is 1 if and only if its inputs are different:

7. A NOT
gate's output is different from its input:

**Encode the specific problem instance**

The
circuit shown in Figure 8.4 is encoded as circuit *C1* with the following description.

First, we
categorize the gates:

Type(X1)=
XOR Type(X2)= XOR

**Pose queries to the inference procedure**

What
combinations of inputs would cause the first output of Cl (the sum bit) to be 0
and The second output of C1 (the carry bit) to be l?

**Debug the knowledge base**

We can
perturb the knowledge base in various ways to see what kinds of erroneous

behaviors

emerge.

**Usage of First Order Logic.**

The best
way to find usage of First order logic is through examples. The examples can be
taken from some simple **domains.** In
knowledge representation, a domain is just some part of

the world
about which we wish to express some knowledge.

**Assertions and queries in first-order logic**

Sentences
are added to a knowledge base using TELL, exactly as in propositional logic.

Such

sentences
are called **assertions.**

For
example, we can assert that John is a king and that kings are persons:

*TELL(KB, King (John))*

Where KB
is knowledge base.

*TELL(KB, x
King(x) => Person(x))*.

We can
ask questions of the knowledge base using ASK. For example, returns *true.*

Questions
asked using ASK are called **queries**
or **goals** ASK(KB,Person(John))

Will
return true.

(ASK KBto
find whther Jon is a king) ASK(KB, **x**
person(x))

.

**The kinship domain**

The first
example we consider is the domain of family relationships, or kinship. This
domain includes facts such as

"Elizabeth
is the mother of Charles" and

"Charles
is the father of William7' and rules such as "One's grandmother is the
mother of one's parent." Clearly, the objects in our domain are people.

We will
have two unary predicates, *Male* and *Female.*

Kinship
relations-parenthood, brotherhood, marriage, and so on-will be represented by
binary predicates: *Parent, Sibling,
Brother, Sister, Child, Daughter,Son, Spouse,* *Husband, Grandparent, Grandchild, Cousin, Aunt, *and* Uncle.*

We will
use functions for *Mother* and *Father.*

**Forward chaining with an example.**

Using a
deduction to reach a conclusion from a set of antecedents is called forward
chaining. In other words,the system starts from a set of facts,and a set of
rules,and tries to find the way of using these rules and facts to deduce a
conclusion or come up with a suitable couse of action. This is known as data
driven reasoning.

The proof
tree generated by forward chaining.

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 of America, hassomemissiles, and all of its missiles were sold
to it by Colonel West, who is American.

• Prove
that Col. West is a criminal

... it is
a crime for an American to sell weapons to hostile nations: American(x)

Note:

(a)The
initial facts appear in the bottom level

(b) Facts
inferred on the first iteration is in the middle level

(c) The facts
inferered on the 2nd iteration is at the top level

ALGORITHM

**Backward chaining with an example.**

Forward
chaining applies a set of rules and facts to deduce whatever conclusions can be
derived. In backward chaining ,we start from a conclusion, which is the
hypothesis we wish to prove and we aim to show how that conclusion can be
reached from the rules and facts in the data base. The conclusion we are aiming
to prove is called a goal, and the reasoning in this way is known as
goal-driven.

**Backward chaining example**

Note:

(a)
To prove Criminal(West) ,we have to prove four
conjuncts below it.

(b)
Some of which are in knowledge base,and others
require further backward

**UNIFICATION:**

UNIFY(P,R)=UNIFY(Q,R)=UNIFY(P,Q)

RESOLUTION:

v NF

v CNF

v INF WITH REFUTATION

v CNF WITH REFUTATION

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Artificial Intelligence : Logical Agents |

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