Lect Topic2 Sem1 0910
Transcript of 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
if-then Rules or Production Rules
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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
if-then Rules or Production Rules
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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.
if-then Rules or Production Rules
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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