Chapter 1(ppt) - Ahmad Falah Aljaafreh, Ph.D.

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19 Οκτ 2013 (πριν από 4 χρόνια και 8 μήνες)

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Dr. Ahmad aljaafreh

What is AI?

“AI” can be defined as the simulation of human intelligence on a
machine, so as to make the machine efficient to identify and use the right
piece of “Knowledge” at a given step of solving a problem.

A system capable of planning and executing the right task at the right
time is generally called

What is AI?

subject dealing with computational models that can think and act

Does rational thinking and acting include all possible
characteristics of an intelligent system?

learning, perception and planning.

General Problem Solving

Approaches in AI


: status of the solution at a given step of the problem solving


The problem solving procedure applies an

to a state to get the

next state.

transition to the next state, thus, is continued until the goal (desired) state is

derived. Such a method of solving a problem is generally referred to as state

space approach.


puzzle problem

two operations, blank
up (BU) / blank
down (BD) and blank
left (BL) / blank
right (BR)


Algorithm for solving state
space problems


1. state: = initial
state; existing

While state ≠ final state do


a. Apply operations from the set {BL, BR, BU,

BD} to each state so as to generate new

b. If new
states ∩ the existing
states ≠ φ

Then do

Begin state := new


states := existing



End while;


Algorithm for solving state
space problems

trick in solving problems by the state
space Approach


determine the set of


use it at appropriate
of the problem.

AI problems and non

Generally, problems, for which straightforward mathematical /

logical algorithms are not readily available and which can be solved by

intuitive approach only, are called
AI problems


jug problem, Travelling Salesperson, diagnosis problems, and pattern

The key to AI approach is intelligent search and matching. In an intelligent

search problem / sub
problem, given a goal (or starting) state, one has to
reach that state from one or more known starting (or goal) states

how to control the generation of states?

Some of the well
known search algorithms are:

a) Generate and Test

b) Hill Climbing

c) Heuristic Search

d) Means and Ends analysis

Generate and Test Approach:

Generation of the state
space from a known starting state (root)

Continues expanding the reasoning space until the goal node


After generation of each and every state, the generated node is

compared with the known goal state.


When the goal is found, the algorithm terminates.


Does not allow filtering of states.

(2) Hill Climbing Approach:

total cost for reaching the goal from the given starting state (f).


While f ≤ a predefined utility value and the goal is not reached,

new nodes are generated of the current node.

in case all the neighborhood nodes (states) yield an identical

value of f and the goal is not included in the set of these nodes,

the search algorithm is trapped

to overcome this problem is to select randomly a new starting

state and then continue.

Example: trigonometric identities proof.

(3) Heuristic Search:


use one or more
heuristic functions to determine

the better candidate states among a set of legal states.

The heuristic function measures the fitness of the candidate states.

(d) Means and Ends Analysis:

attempts to reduce the gap between the current state and the goal state.

measure the distance between the current state and the goal, and then

apply an operator to the current state, so that the distance between the

resulting state and the goal is reduced.

Example: mathematical theorem

proving processes.

Good general problem solving techniques in AI:

Problem Decomposition

Constraint Satisfaction:

evaluate the variables X1, X2 and X3 from the following set of constraints:

{ X1 ≥ 2; X2 ≥3 ; X1 + X2 ≤ 6; X1 , X2 , X3

I }.

The Disciplines of AI

Topics which we find significant and worthwhile to understand

Learning Systems:

Knowledge Representation and Reasoning:

Knowledge Acquisition:

Intelligent Search:

Logic Programming:

Soft Computing:

Fuzzy, Neural, Genetic

Applications of AI Techniques


Expert Systems:

consists of a
knowledge base
database and an inference


Image Understanding and Computer Vision:


Navigational Planning for Mobile Robots:

camera or ultrasonic sensors

static and dynamic environments


Speech and Natural Language Understanding:


Intelligent Control & Scheduling:

A Brief History of AI

Professor Peter Jackson of the University of Edinburgh classified the history

of AI into three periods namely i) the classical period (of game playing and

theorem proving), ii) the romantic period, and iii) the modern period the
major research work carried out during these periods is presented below.

The Classical Period

This period dates back to 1950. The main research works carried out
during this period include game playing and theorem proving. The
concept of statespace approach for solving a problem, which is a
useful tool for intelligent problem
solving even now, was originated
during this period.

Turing’s “test”, which is a useful tool to test machine intelligence,

originated during this period.

The Romantic Period

The romantic period started from the mid 1960s and continued until the mid

1970s. During this period, people were interested in making machines

“understand”, by which they usually mean the understanding of natural


The Modern Period

The modern period starts from the latter half of the 1970s to the present day.

This period is devoted to solving more complex problems of practical