Artificial Intelligence chapter 2 a Knowledge representation and search, Lecture notes of Artificial Intelligence

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Artificial Intelligence

(Part 2)

Knowledge Representation and Search

Course Contents

Again..Selected topics for our course. Covering all of AI is impossible!

Key topics include: Introduction to Artificial Intelligence (AI) Knowledge Representation and Search Introduction to AI Programming Problem Solving Using Search Exhaustive Search Algorithm Heuristic Search Techniques and Mechanisms of Search Algorithm Knowledge Representation Issues and Concepts Strong Method Problem Solving Reasoning in Uncertain Situations Soft Computing and Machine Learning

AI as representation and search

Artificial Intelligence :- study of

representation and search through which

activity can be performed on a

mechanical device (engineering

perspective)

Knowledge Representation

Example: given the headline “ Najib wins election”.

Could a machine answer the question “ Who is prime

minister?”

Background knowledge is necessary

How is this knowledge written down, or encoded so a

computer can use it?

How can it be written down efficiently? We can't write

everything down.

What do the formal representations mean?

Semantics.

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Why need representation?

-capture important features of a problem

-make information accessible to problem solving

procedure

-abstraction

e.g.

The real number: p

Decimal : 3.1415927….

Floating point : 31416 1

mantissa exponent

Computer memory: 11100010

AI as representation

A representational scheme should:

1. Be adequate to express all of the necessary

information

2. Support efficient execution of the resulting

code

3. Provide a natural scheme for expressing the

required knowledge

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2. Allow new knowledge to be inferred from set of facts and rules

 X  $Y on(X,Y)  clear (X)

• for all X, X is clear if there does not exist a Y such that Y is on X-

3. Allow representation of general principles as well as specific solutions
4. Capture complex semantic meaning

hassize(bluebird, small), hascovering(bird,feathers), hascolor(bluebird,blue), hasproperty(bird,flies)

5. Allow for meta-level reasoning

“knowing about what you know” – meta-knowledge

"Meta-" is used to designate something that applies to the thing as a whole, often including itself. For instance, a meta-language is a language that used to talk about language.

AI as representation

AI as representation

Knowledge representation languages:

Predicate calculus, semantic networks,

frames, objects, rules,..etc.

AI as representation and

search

Problem solving as search:

Problem are solved by searching among alternative

choices, is supported by a commonsense view of

human problem solving.

Search

Often no direct way to solve a problem.

You may know what moves are allowed

but not how to put the moves into a

sequence to solve a problem.

Can generate possibilities for next step

and so on.

Considering full search space often too

expensive. Too many possibilities (even

for computers).

Rubik's Cube

43,252,003,274,489,856,000 combinations

Up to 481,229,803,398,374,426,442,198,455,156,

brute-force solution attempts

More than 15,259,696,962,150,381 years!!

Need to look at heuristics or strategies, i.e. selecting the

best options to lead to a solution.

Reasoning and Inference

If we know that elephants are mammals with

four legs and that Barbar is an elephant, can we

conclude that Barbar is a mammal with four

legs?

How do we deal with an elephant (and a

mammal) but only has three legs?

If we use formal logic as a knowledge

representation language, logical proof can be

used to allow us to infer new facts.

Intelligent activity is achieved through the use of:

1. Symbol or pattern to represent significant

aspects of a problem domain

2. Operations on these patterns to generate

potential solutions to problems

3. Search to select a solution from among

these possibilities

Next…

 Propositional calculus

 Predicate calculus