Lecture 26
Artificial Intelligence
Can Machines be intelligent?
This is a difficult question and to answer it we need to define intelligence
and thinking.
Some aspects of intelligence:

Planing

Learning

Reasoning

Problem Solving

Perception
In the 1950's, a test was proposed by Alan Turing to determine whether
a machine is intelligent or not  The Turing Test.
The Turing Test
The basis of the turing test is for a person to attempt to distinguish
between a machine and a human being. For fairness, the machine and the
human are isolated from the person carrying out the test and messages are
exchanged via a keyboard and screen. If the person can not distinguish
between the computer and the human being, then the computer must be intelligent.
This test is often criticised because it only tests a limited aspect of
intelligence.
Some people think that even if a machine could pass the Turing Test
it may still not be intelligent.
Each year, there is a Turing test contest called the Loebner Prize.
This is a transcript from the winner for 1998.
The Chinese Room Problem
A (non Chinese speaking) person is locked in a room and given the algorithm
for a program that could pass the Turing test in Chinese. He/she is asked
questions in Chinese, applies the algorithm by hand, and feeds the answers
back. The room will appear intelligent but the person inside understands
no Chinese, so is there any intelligence present?
This problem is criticised because, it may well be possible for the
complete system to be intelligent (i.e. room and person inside) without
the person being intelligent.
Some people say that the passing the Turing test is sufficient
to prove intelligence but it is not necessary to prove intelligence.
In other words, a machine may fail the Turing Test but still be intelligent.
There are plenty of examples of computer systems that perform tasks
that would require intelligence if they were performed by a human being.
Types of AI Tasks
One possible classification of AI tasks is into 3 classes: Mundane problems,
Formal problems and Expert Problems.
Mundane Tasks

Perception

Vision

Speech

Natural Language understanding, generation and translation

Commonsense Reasoning

Simple reasoning and logical symbol manipulation

Robot Control
Formal Tasks

Games

Chess
Deep Blue  last year beat Gary Kasparov

Backgammon

Draughts

GO
To solve these problems we must explore a large number of solutions quickly
and choose the Best One.

Mathematics

Geometry and Logic
Logic Theorist: It proved mathematical theorems. It actually proved
several theorems from Classical Math Textbooks

Integral Calculus
Programs such as Mathematica and Mathcad and perform complicated symbolic
integration and differentiation.

Proving Properties of Programs e.g. correctness
Manipulate Symbols and reduce problem (usually recursively), until
the answer is obvious. That is, it can be looked up in a table.
Expert Tasks

Engineering

Design

Fault finding

Manufacturing

Planning

Scientific Analysis

Medical Diagnosis

Financial Analysis
Rule based systems 
if (conditions) then
action
Example of AI problem solving.
Problem : A farmer has a hungry fox a fat goose and a bag of grain. The
farmer needs to cross a river but his boat can only carry two things.
Constraints: Fox and goose cannot be left together Goose and grain cannot
be left together.
How to cross the river?
English language representation is hard to solve.
Try visual/graphical representation:
This is called a search tree. The search tree is not a
data structure like the trees we saw earlier. It is a representation of
the problem that allows us to solve it.
To solve this problem we need only follow the tree from its root node
to any leaf node.