Lect Topic2 Sem1 0910

8/9/2019 Lect Topic2 Sem1 0910

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CSC 3301CSC 3301 -- Principles Of Artificial IntelligencePrinciples Of Artificial Intelligence

KNOWLEDGE REPRESENTATIONKNOWLEDGE REPRESENTATION

Assoc. Prof. Dr. Normaziah Abdul Aziz

Semester 1 2009/2010


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Knowledge facts, information and rules that are known

in a particular field(s) that can also be used for

contextualisation on a given situation or events related.

[Please note that this definition of knowledge is from the perspective ofinformation fed and used in the machine / computer, nor that of the human

intellectual capacity.The definition of Knowledge (ilm) in Islam is the arrival (husul) in the soul of the

meaning of a thing or an object of knowledge. Refer Syed Muhammad Naquib

Al-Attas, The Concept of Education in Islam, pp 17]

Representation a collection of objects that can be referred to for other

symbols or physical objects. We need representation of an object or situationwhen we cant access the real thing or situation itself.

to capture critical features of a problem

What is Knowledge Representation (KR)?


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Representation that is used to represent a problem isimportant.

The way the computer represent a problem, the variables it

uses, the operators it applies to those variables.

All the above will make a difference between an efficient

algorithm and an algorithm that doesnt work.

The Need for a Good Representation

Examples of data representationExamples of data representation

Array, Lists, Stacks of data (text, numerical, etc)

Picture point (pixel) represent an image intensity or gray level


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Example A scenario where you dropped your contact-

lens at the football field of IIUM stadium. How can you find it?

What does it take for you to locate it?


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1st representation


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2nd representation


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3rd representation

Same representation but different level

of granularity or refinement

Useful, efficient and meaningful representation is

essential.

The representation

relate to the problembeing solved, avoid

pointless computation.


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A Quick look of Examples of KR in AI

Semantic netshumanhuman

aliali kittykittyahmedahmed

catcat

is_a is_a is_a

ownshas_friend

Ahmed has a friend named Ali, and a cat called Kitty.Ahmed has a friend named Ali, and a cat called Kitty.

If-then Rules

IF there are 6 flat surfaces

AND the size and shape of each surfaces is the same

THEN the object has a cubic shape


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is_a (kitty, cat)owns (ahmed, kitty)

friend_of (ali, ahmed)

is_a (ahmed, human)

is_a (ali, human)

Frames

AhmedAhmed

is_a Humanis_a Human

owns Kittyowns Kittyfriend Alifriend Ali

KittyKitty

is_a Catis_a Cat

owned by Ahmedowned by Ahmed

HumanHuman

has_num_leg 2has_num_leg 2

has_brain truehas_brain true

AliAli

is_a Humanis_a Human

friend_of Ahmedfriend_of Ahmed

Logic

A Quick look of Examples of KR in AI


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The Knowledge Representation

The knowledge-based system

a model of something in the real world

designed by modeling the knowledge and reasoning mechanism

usually encoded by a human expert

The Representation (Knowledge Base)

Syntax (structure of the elements)

Semantics (meaning of the elements)

The Inference Engine

The ability to find implicit knowledge by reasoning over the explicit

Knowledge Base

Inference system

Knowledge Based system


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The Representation

A Knowledge Representation language is a system for

encoding knowledge.

It is defined by a syntax and semantics.The representation limits what kind of knowledge can be

represented and reasoned about.

Syntax

The notational aspects.

How to encode knowledge explicitly.

Grammatical rules the symbols to use and the way to

combine them.

Similar to natural language, where correct sentence structure(e.g. subject-verb-object)

Example of different syntax expressing same content:

(green my-car)

green(my-car)

my-car.green = true


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Semantics

Concerns the meaning of symbols.

What does symbol represent?

Example

If a system knows that green(my-car), it knows that

some property (green) is true about somesymbol(mycar), but what does my-car and green

mean?

The representation limits what kind of knowledge can be

represented and reasoned about.

Example

The system knows that all cars have engines and a

Toyota is a car. Will it be able to infer that Toyota has an

engine?


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Knowledge and Inference

Inference - The ability to find implicit knowledge by reasoning

over the explicit knowledge

A Knowledge Representation must not be seen in isolation from

its inference capabilities.

Realised through a set of algorithm the inference engine.

An inference engine implements a set of inference rules.

The inference engine infers implicit knowledge.

The inference rules are abstract rules that can be applied in

various domains (domain independent)


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Example of Knowledge & Inference - Inheritance

Inference in Semantic Nets.

Property of superclass is inherited to subclass

Example:

The computer can infer that Tweety moves by flying

birdbird

tweetytweety flyingflying

is_a moves_by


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Example Modus Ponens

Reasoning mechanism in Logic.

Says that some proposition always follow from another.

Example

The computer can infer that Tweety moves by flying

is_a(tweety, bird).

moves_by(y, flying) :- is_a(y, bird).


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What makes a good representation?

Completeness we can express what we want to

express.

Conciseness we can do so without ambiguities.

Transparency It is easy to understand, common

understanding between human and computer.

Computational efficiency the inference engine is

efficient

Note: There is tradeoff between these criteria.


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Types of Knowledge RepresentationTypes of Knowledge Representation

often usedoften used


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if-then Rules or Production Rules

if-then rules is also referred to as Production rules

By far, the most popular formalism for representing

knowledge

Rules are conditional statements but they can have various

interpretations that we can define:

ifprecondition Pthen conclusion C

ifsituation S then action A

ifconditions C1 and C2 hold then condition C does not

hold

Rules can represent relations, recommendations, directives,

strategies and heuristics


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RelationIF the fuel tank is empty

THEN the car engine cant be started

Recommendation

IF the season is autumn

AND the sky is cloudy

AND the forecasted weather is drizzle

THEN the advice is take along an umbrella

Directive

IF the car is dead

AND the fuel tank is empty

THEN the action is refuel the car

Some examples of Production Rules


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StrategyIF the car is dead

THEN the action is check the fuel tank;

step 1 is complete

IF step 1 is completeAND the fuel tank is not empty

THEN the action is check battery;

step 2 is complete

Heuristic

IF the spill is liquid

AND the spill pH is < 6

AND the spill smells vinegar

THEN the spill material is acetic acid

Some examples of Production Rules


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The components of a rule-based system have the form:

if < condition > then < conclusion > or

if < antecedent > then < consequent >

A rule can have more than 1 antecendents and more than1 consequents

Rules can be evaluated by:

backward chaining

forward chaining



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if-then rules turn out to be a natural form of expressingknowledge, and have the following features:

modularity each rule defines a small, relatively

independent piece of knowledge

incrementability - new rules can be added to theknowledge base relatively independent of other rules

modifiability (as a consequence of modularity) old

rules can be changed relatively independent of other

rules

support systems transparency



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if-then rules also support knowledge that has probabilityof truth value (i.e. for knowledge that is not absolutely true

for all conditions)

In such cases, we modify the rules by adding likelihood

qualifications to their logical interpretation, as example:ifcondition A then conclusion B follows with certainty

value F

IF

1 condition is primary bacteremia, and

2 the site of the culture is one of the sterilesites, and

3 the sustected portal of entry of the organism is the gastrointestinal tract

THEN

there is suggestive evidence (0.7) that the identity of the organism is

bacteroides.

Sample rule extracted from Mycin Expert system.



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Forward Chaining in Production Rules

Given some facts, work forward through inference net.

Discovers what conclusions can be derived from data.

Inference begins here


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Backward Chaining in Production Rules

To determine if a decision should be made, work backwards

looking for justifications for the decision. Eventually, a decision must be justified by facts.

Inference begins here


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A semantic network is a directed graph consisting of nodesA semantic network is a directed graph consisting of nodes

(also termed points or vertices) which represent(also termed points or vertices) which represent conceptsconcepts andand

edges (also termed lines or arcs) which representedges (also termed lines or arcs) which represent semanticsemantic

relationsrelations between the concepts.between the concepts.

Semantic network problem can be transformed to logicSemantic network problem can be transformed to logic

As example:As example:

A graph with 6 vertices (concepts) and 7

edges (semantic relations).

Semantic Network (SN)

camelmammal

brown

is-a

colour

( x) (camel(x) -> mammal(x))

colour(camel, brown)


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Antonymy: Opposite meaning. Example: Cold is the opposite of warm

Synonymy: Equivalent meaning. Example: Cheap is synonym to inexpensive.

Causal relation: A is the cause of B. Example: Scurvy is caused by lack of vitamin C.

Homonym . Two concepts,A and B, are expressed by the same symbol.Example:Both a financial institution and a edge of a riverare expressed by the word bank (the word has two

senses).

Active relation: A semantic relation between two concepts, one of which

expresses the performance of an operation or process affecting the

other. Example: The room temperature is high and the ice cubes melt.

Temporal relation: A semantic relation in which a concept indicates a time or

period of an event designated by another concept. Example: Second World War, 1939-1945.

Some Semantic Relations in SN


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Some Semantic Link terms

Is-a

A-kind-of

Type-kind

Whole-part

Part-of

Instance-of Attribute-of

Has-parts

Connected-to

Made-of Has-attribute

Object-property

Object-action

Action-result Object-example


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State: I own a tan leather chair.

Example of Semantic Network


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Event: John gives the book to Mary.

Example of Semantic Network


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Frames

A network representation, as semantic nets

Frames have links between concepts, but each conceptcontains more knowledge

Frames represent a concept either a class or individual

The concept of a Frame is defined by a collection ofSlots

Each frames have a number of slots

Slots are pairsA value can be set of values, any primitive data-types, a

pointer to another frame or a function (procedure

attachment)

Reasoning in Frames

can be done by MatchingorInheritance

< works-as Computer Scientist>

>

Example a person description Frame


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More on Knowledge Representation (KR)

Awell referred paper on Knowledge Representation:

Randall Davis, Howard Shrobe and PeterSzolovits,

What is a Knowledge Representation?,

AAAI,S

pring1993.


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Roles if KR (as discussed in the paper)

Role 1: A KR is a surrogate

How close is a representation to the real thing? What kind

of simplifying assumptions are made?

Role 2: A KR is a set of Ontological Commitments

What kind if Ontological commitments does thisrepresentation force on you?

Role 3: A KR is a Fragmentary Theory of Intelligent Reasoning

What are the sanctioned and recommended inferences?

Role 4: A KR is a medium for efficient computation

Role 5: A KR is a medium of human expression


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Given the following information:

A bird is a kind of animal. Flying is the normal movingmethod for birds. Birds are active during daylight.

An albatross is a bird. Albatross is black and white in

colour.

Albert is an albatross, and so is Ross.

Kiwi is a bird that moves by walking.

Kiwi is active at night and brown in colour.Kim is a Kiwi.

Convert the statements above in any of the 2

representation schemas below: Semantic net

Rule-based

Frame.


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BathBath--

roomroomHallHall

KitchenKitchen

WindowWindow

A toy knowledge base

Diagnosing the problem of water leaking in an apartment.

A problem can arise either in the bathroom or in the

kitchen. In either case, the leakage also causes a problem

(water on the floor) in the hall.

Try diagnose the possible causes of water leaking in

the apartment, when the:

a) Hall is wet

b) Kitchen is wet

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Lect Topic2 Sem1 0910