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

AI and Robotics

Oct 19, 2013 (4 years and 7 months ago)

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AI

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
rational
.

What is AI?

subject dealing with computational models that can think and act
rationally.

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

learning, perception and planning.

General Problem Solving

Approaches in AI

state

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

Procedure.

The problem solving procedure applies an
operator

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.

Example

4
-
puzzle problem

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

state
-
space

Algorithm for solving state
-
space problems

Begin

1. state: = initial
-
state; existing
-
state:=state;

2.
While state ≠ final state do

Begin

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

BD} to each state so as to generate new
-
states;

b. If new
-
states ∩ the existing
-
states ≠ φ

Then do

Begin state := new
-
states

existing
-
states;

Existing
-
states := existing
-
states

{states}

End;

End while;

End.

Algorithm for solving state
-
space problems

trick in solving problems by the state
-
space Approach

1
-

determine the set of
operators

2
-

use it at appropriate
states
of the problem.

AI problems and non
-
AI
problems.

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
.

Examples:

water
-
jug problem, Travelling Salesperson, diagnosis problems, and pattern
classification.

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

(1)
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
engine

-

Image Understanding and Computer Vision:

-

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

languages.

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

interest.