Knowledge representation
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Knowledge Representation. KR encompasses: The structure used to describe elements of Knowledge The interpretive process required to use the described knowledge Components: Factual Knowledge (Facts) Procedural Knowledge (Rules). Representation Evaluation.

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Knowledge Representation

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Knowledge representation

Knowledge Representation

KR encompasses:

  • The structure used to describe elements of Knowledge

  • The interpretive process required to use the described knowledge

    Components:

  • Factual Knowledge (Facts)

  • Procedural Knowledge (Rules)


Representation evaluation

Representation Evaluation

Transparency: Easy identification of knowledge

Explicitness: Direct representation

Naturalness: Represent without transformation

Efficiency: easy access of knowledge

Adequacy: Complete knowledge representation

Modularity: Fragmentations-independent storage


Level of representation

Level of Representation

  • Low Level Knowledge

    The fundamental knowledge used to derive further knowledge

    Example: The law of Gravity

    (also known as first principle knowledge)

  • High Level Knowledge

    The derived knowledge from the first principle knowledge. This uses inferencing methods


Formal logic

Formal Logic

  • The most widely used formal logic method is

    FIRST-ORDER PREDICATE LOGIC

    Components :

    Alphabets

    Formal language

    Axioms

    Inference Rules


Alphabets i

Alphabets-I

Predicates, variables, functions,constants, connectives, quantifiers, and delimiters

Constants: (all capital letters)

BLUEa color

SANTROa car

CROWa bird

Variables: (all lower case letters)

dogan element that is a dog, but unspecified

coloran unspecified color


Alphabets ii

Alphabets-II

Function:

father(ALI)A function that specifies the unique element, that is the father of ALI

killer(x)x is a killer

Predicate

MAN(SHAHID)A predicate which gets TRUTH value equal to 1 (or represented by T) when the interpretation is true. Here Shahid is a man so the predicate is true.

BIGGER(ALI,father(BABAR))Ali is bigger than the father of Babar.


Alphabets iii

Alphabets-III

Connectives:

^and

vor

~not

Implication

Quantification

Universal quantifiers

Existential quantifiers


Examples

Examples

My house is a blue, two-story, with red shutters on the corners

BLUE(MY-HOUSE)^TWO-STORY(MY-HOUSE)^RED-SHUTTERS(MY-HOUSE)^CORNER(MY-HOUSE)

Ali bought a scooter or a car

BOUGHT(ALI,CAR)vBOUGHT(ALI,SCOOTER)

IF fuel, air and spark are present the fuel will combust

PRESENT(SPARK)^PRESENT(FUEL)^PRESEN(AIR)

COMBUSTION(FUEL)


Examples1

Examples

All people need air

Vx[PERSON(x) NEED_AIR(x)]

The owner of the car also owns the boat

[OWNER(x,CAR)^OWNER(x,BOAT)]

Formulate the following expression in the PC:

“Ali is a computer science student but not a pilot or a football player”


Examples2

Examples

Restate the sentence in the following way:

  • Ali is a computer science (CS) student

  • Ali is not a pilot

  • Ali is not a football player

    CS_STUDENT(ALI)^

    ~PILOT(ALI)^

    ~FOOTBALL_PLAYER(ALI)


Examples3

Examples

Studying expert systems is exciting and applying logic is very fun if you are not going to spend all of your time slaving over the terminal

Vx(~SLAVE(x) [ES_ECITING(x)^LOGIC_FUN(x)])

Every voter either favors the amendment or despises it

Vx[VOTER(x) [FAVOR(x,AMENDMENT) v DESPISE(x,AMENDMENT)]

^ ~[FAVOR(x,AMENDMENT) v DESPISE(x,AMENDMENT)[)]


Non formal methods

Non-formal Methods

Rule Based Method

A rule based system consists of a set of IF-THEN rules, a set of facts normally representing things that are currently held to be true, and some interpreter controlling the application of the rules, given the facts

Control Scheme

Rules

IF ------ THEN ADD --------

IF ----- AND ----- THEN ADD -----

Database of facts

Initial facts


Reasoning methods

Reasoning Methods

Forward Chaining Method

Data Driven:

Starts from the initial facts and adds new conclusions to the facts database and tries to reach the conclusions

Backward Chaining Method

Goal Driven:

Starts from the goal and looks for the premise of the conclusion in the database, followed by redefining the goal and so on.


Forward chaining method

Forward Chaining Method

Algorithm

Repeat:

  • Find all the rules which have conditions (IF part) satisfied

  • Select one, using conflict resolution strategies (which rule to be selected first if there more than one satisfy the conditions).

  • Perform actions in conclusions, possibly modifying current working memory (database of facts).


Forward chaining method ii

Forward Chaining Method-II


Inference chain

Inference Chain


Forward chaining method1

Forward Chaining Method

Facts:

F1:alarm_beeps

F2:hot

Rules:

R1: IF hot AND smoky THEN ADD fire

R2:IF alarm_beeps THEN ADD smoky

R3: IF fire THEN ADD switch_on_sprinklers


Conflict resolution

Conflict Resolution

  • Prefer rules that involve facts that have been recently added to the working memory

  • Prefer rules with more specific facts

  • Allow user to prioritise the rules


Forward chaining method2

Forward Chaining Method

Facts:

F1:alarm_beeps

F2:hot

F3:dry

Rules:

R1: IF hot AND smoky THEN ADD fire

R2:IF alarm_beeps THEN ADD smoky

R3: IF fire THEN ADD switch_on_sprinklers

R4: IF dry THEN ADD switch_on_humidifiers

R5: IF sprinklers_on THEN DELETE dry

R6: IF hot THEN ADD summer


Backward chaining method

Backward Chaining Method

Algorithm

To prove a goal:

  • If G is in the initial facts it is proven

  • Otherwise, find a rule which can be used to conclude G, and try to prove each of that rule’s pre-conditions. G is then proved true if all the pre-conditions are proved true.


Backward chaining method1

Backward Chaining Method


Backward chaining method2

Backward Chaining Method

G: switch on the sprinklers

Facts:

F1:alarm_beeps

F2:hot

Rules:

R1: IF hot AND smoky THEN ADD fire

R2:IF alarm_beeps THEN ADD smoky

R3: IF fire THEN ADD switch_on_sprinklers


Backward chaining method3

Backward Chaining Method

Let G=G1: switch on the sprinklers

Matches conclusion of R4, precondition of R4 have to be satisfied and becomes new goal

New Goal G2: fire

Matches conclusion of R1, precondition of R1 have to be satisfied and becomes new goal

New Goals G3:smoky, G4: hot

G3 Matches conclusion of R2, thus new goal is alarm beeps

G4 is already in the initial facts so is true

New Goal is G5: alarm_beeps, G4: hot

Both are included in the initial facts thus the initial Goal G is true


Example

Example

Facts:

Nil

Rules:

R1: IF coughing THEN ADD smoky

R2: IF wet AND NOT raining THEN ADD burst_pipe

R3: IF NOT coughing AND alarm_rings THEN ADD

burglar

R4: IF smoky AND hot THEN ADD fire

Specify the Line of Questioning and implement backward chaining method to prove burglary


Example1

Example

Interaction between the user and the ES:

System: Are you coughing?.

User: No.

System: Are you getting wet?.

User: No.

System: Is there an alarm ringing?.

User: No.


Example ii

Example-II

Suppose we are trying to develop a safety system for the a chemical plant. If some liquids spills over and if it is flammable the situation is dangerous and the fire department should be called. In order to check that what type of liquid is spilled one should check the smell of the liquid and the pH value of the material. If the smell is like vinegar and the pH is less than 6, the material is definitely acetic acid.

Specify the Line of Questioning and implement backward chaining method to prove material being acetic acid and should the fire deaprtment be called or not


Rules

Rules

R1: If a flammable liquid was spilled, then call the fre department

R2: If the pH of the spill is less than 6, then the spill material is an acid

R3: If the spill material is an acid, and the spill smells like vinegar, then the spill material is acetic acid

Ph less

than 6

Material acid

Acetic Acid

Smell like

vinegar


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