Introduction to Artificial Intelligence
What is AI?
'Artificial Intelligence may be defined as a
branch of computer science that is concerned
with the automation of intelligent behaviour'.
Luger & Stubblefield.
1940 - present: computer engineering. First
operational computer (Alan Turing's team) 1940.
1950 - Turing developed the Turing Machine.
This demonstrated that a machine could
manipulate symbols as well as numbers.
Later became known for the Turing Test - for
comparisons of human and machine intelligence.
1943 - McCulloch & Pitts initiated research on
Neural Networks
1956 - AI term first used in print by John
McCarthy at Dartmouth College Conference,
USA.
McCarthy also invented the AI language LISP
(LISt Processing).
1960s - MIT (Massachusetts Institute of
Technology became v. important for AI research
- see web site).
1976 - Newell & Simon developed the GPS
(General Problem Solver). Problems defined in
terms of initial & goal situations. Contained
operators that determined how to move from one
state to the next. Problems with size of search
space & with methods of knowledge
representation.
This idea of State Space Search is still used in
today's AI systems.
Later 60s & 70s - apparent that GPS approach
gave weak performance. Started developing
systems in specific knowledge domains - birth of
Knowledge Based Systems (KBS) or Expert
Systems (ES).
DENDRAL - to identify chemical structures of
unknown molecules.
MYCIN - medical system for selection of
antibiotics.
LUNAR '80s - more success commercially for AI
products
'86 to present - Neural network (sub-symbolic)
& KBS (Symbolic) research continues.
There have been both successes & failures in ES
developments.
What advantages do computers have over
human experts?
 Humans can change jobs, become ill, have off
days etc.
 Human expertise is difficult to transfer.
 Human expertise is expensive.
BUT:
 Humans are creative, inspired, flexible, have
common sense, good learning capabilities.
Philosophy of AI
John Searle introduced terms WEAK AI and
STRONG AI.
STRONG view of AI is that the human brain is
no more than a physical symbol manipulation
system. Hence with enough computer power it
could be replicated.
WEAK view of AI says that computer systems
are only a simulation of intelligence. They are
useful for understanding cognitive processes but
should not assume that simulation is a reality.
Knowledge Based Systems
What is Knowledge?
'The symbolic representation of aspects of some
named universe of discourse' - Winston 1984.
This assumes we can symbolise knowledge, i.e.
represent it. Simon (1969) & others see AI as
concerned with symbolic processing.
Feigenbaum's definition of a KBS is:
'An intelligent computer program that uses knowledge &
inference procedures to solve problems that are difficult
enough to require significant human expertise for their
solution. Knowledge necessary to perform at such a level,
plus the inference procedures used, can be thought of as a
model of the expertise of the best practitioners in the field.'
A KBS generally does the following:
 Represents & stores knowledge
 Provides inferencing abilities
 Includes a consistent user interface
 Incorporates a means to connect to traditional
software e.g. databases.
Some terminology:
 Knowledge representation - knowledge is
stored in a knowledge base in a form most
appropriate to the given application. There are
a number of methods of representation.
 Domain expert - provides expertise for the
system being modeled.
 Knowledge elicitation/acquisition Extraction of knowledge from one or more
experts in a domain
 Knowledge engineer - implements a KBS
Neural Networks
Ref: http://blizzard.gis.uiuc.edu/htmldocs/Neural/neural.html
 Neural networks are typically organized in layers.
 Layers are made up of a number of interconnected
'nodes' which contain an 'activation function'.
 Patterns are presented to the network via the 'input
layer', which communicates to one or more 'hidden
layers' where the actual processing is done via a system
of weighted 'connections'.
 The hidden layers then link to an 'output layer' where
the answer is output as shown in the graphic below.
 Most ANNs contain some form of 'learning rule' which
modifies the weights of the connections according to the
input patterns that it is presented with.
 ANNs learn by example as do their biological
counterparts; e.g. a child learns to recognize dogs from
examples of dogs.
Genetic Algorithms
 GAs are searching techniques based on the
principles of natural selection and natural genetics.
 John Holland proposed them at the University
of Michigan, USA, in the mid-70s.
They are based on the following principles:
 Evolution operates on chromosomes;
 Chromosomes are strings of genes;
 Less fit artificial creatures do not survive due to
natural selection;
 New generation (offspring) inherits properties
from the old generation (parents) by
reproduction;
 Chromosomes that
reproduce more.
are
more
successful
GAs combine the effect of selection with structured,
randomised recombination of genetic material to
perform robust search.