Basic Comp 2

7/31/2019 Basic Comp 2

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Artificial IntelligenceLecture 2


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Problem Solving ,Search and Control

Strategies

General problem solving

Production System Control Strategies

Exhaustive Search


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To solve a particular problem,we need to do: Define the problem precisely.

Analyze the problem.

Isolate and represent the task knowledgethat is necessary to solve the problem.

Choose the best problem solving technique(s)and apply it

General problem solving

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Production System

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Definition

It provides the structure to AI

programs in a way that facilitatesdescribing and performing the searchprocess.


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A Production System consists of : A set of rule

A global database A control strategy

A rule applier

Production Systems


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The process of solving the problem canbe modeled as a production system

Production System


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The categories of the productionsystems:-

Monotonic Production System

Nonmonotonic Production System

Partially Commutative Production System

Commutative Production System

The production system


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To solve a problem it is necessary totake a decision that which rule toapply next during the process of

searching for a solution to a problem

Control Strategies


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Control Strategies

The first requirement of a good controlstrategy is that it cause motion

The second requirement of a good control

strategy is that it be systematic


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control strategies

some basic elements:

Assertions(facts):Each a predicate calculus sentence with no

variables and no connectives other than not.

example

A ball is on a blockA pyramid is above the ball.


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Goal:

Either a sentence with no variables and noconnectives, in which case the goal is to prove thatit is true (inferrable from the facts and the rules),

or an existentially qualified sentence whose variablesare to be assigned values (if possible) given thefacts and the rules.

Example of Goals

Is the pyramid above the block?What's above the block?

Control Strategies


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control strategies

RulesEach a universally qualified implication with a

conjunction of sentences as antecedent and a singlesentence as consequent.

example of rules:

If something is on something, it's alsoabove it.

If a is above b, b is below a.

If b is above c and a is above b, then a isabove


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inference engine

A rule-based system requires some kind of programto manipulate the rules - for example to decide

which ones are ready to fire. (i.e. which oneshave conditions that match the contents ofworking memory). The program that does this iscalled an inference engine, because in many rule-

based systems, the task of the system is toinfer something, e.g. a diagnosis, from the datausing the rules.

control strategies
http://www.cse.unsw.edu.au/~billw/aidict.htmlhttp://www.cse.unsw.edu.au/~billw/aidict.htmlhttp://www.cse.unsw.edu.au/~billw/aidict.htmlhttp://www.cse.unsw.edu.au/~billw/aidict.html


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forward chaining-An inference engineusing forward chaining searches the inferencerules until it finds one where the If clause isknown to be true. We use the data to decide

which rules can fire, then we fire one ofthose rules, which may add to the data inworking memory and then we repeat thisprocess until we establish a conclusion.

control strategies
http://en.wikipedia.org/wiki/Inference_enginehttp://www.cse.unsw.edu.au/~billw/aidict.htmlhttp://www.cse.unsw.edu.au/~billw/aidict.htmlhttp://www.cse.unsw.edu.au/~billw/aidict.htmlhttp://en.wikipedia.org/wiki/Inference_engine


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control strategies

Backward Chaining-An inference engine usingbackward chaining would search the inferencerules until it finds one which has a Then

clause that matches a desired goal. Inbackward chaining, we work back frompossible conclusions of the system to theevidence, using the rules backwards. Thus

backward chaining behaves in a goal-drivenmanner.

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Exhaustive Searches

Exhaustive Searches

Depth first search

Breadth first search

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exhaustive searches

Breadth first search:A complete breadth first search traversesevery node of the tree or graph, startingfrom the root node or starting node, first

processing, checking, or inspecting theroot/starting node. In future we'll just say it"processes" the node. Next it processes theneighbours of the root/starting node (in some

order), and then the neighbours of theneighbours, and so on, until all the nodeshave been processed.

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exhaustive searchesFor the tree shown below, the order of visiting for a

breadth first search would be: A B C D E F G H I J

A

B C

D E F G

H I J

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Exhaustive Searches

Depth First Search:

algorithm:

to depthFirstSearch a tree with root R

if tree is empty

then % we're finished

else

let N1, N2, ..., Nk be the neighbours of R

depthFirstSearch the subtree with root N1depthFirstSearch the subtree with rootN2

...

depthFirstSearch the subtree with root NkNextPrevious


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Exhaustive Searches

For the tree shown below, the order of first visiting for adepth first search would be: A B D H E C F I G J

A

B C

D E F G

H I J

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Exhaustive Searches

Advantages of depth first search-

-> It requires less memory

-> It may find a solution withoutexamining much of the search space at all.

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Exhaustive searches

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Advantages of breadth first searches:-> It will not get trapped exploring blind

alley.

-> if there is a solution ,then it guaranteed

to find it.


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Uses of Multimedia

Multimedia in BusinessMultimedia Databases

Creating powerful database that can store and

distribute digital media as text. This powerfuldatabases enable us to access any type ofmedia from the desktop

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(Click Here)
http://../My%20Documents/lamib/Database.ppshttp://../My%20Documents/lamib/Database.pps


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References

Books:-

Artificial intelligence by

Elaine Rich and Kevin Knightsites:-

http://www.cse.unsw.edu.au/~billw/aidict.html#depthfirst

Continue EndPrevious
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