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Knowledge Representation
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• Varying kinds and degrees of intelligence
occur in people, many animals and some
machines.
• When did it all start?
• What is Artificial Intelligence?
– Study of how to make computers do things at
which, at the moment, people are better.
– The science and engineering of making
intelligent machines.
• So, what is intelligence?
– The computational part of the ability to achieve
goals in the world.
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– By the late 1950s, there were many researchers
on AI, and most of them were basing their work
on programming computers.
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– A machine that passes the test should certainly
be considered intelligent, but a machine could
still be considered intelligent without knowing
enough about humans to imitate a human.
• Avoids requiring that the machine imitate the
appearance or voice of the person.
– The human would try to persuade the observer
that it was human and the machine would try to
fool the observer.
Damien Costello, Dept of Computing &
Maths, GMIT
– He argued that if the machine could
successfully pretend to be human to a
knowledgeable observer then you certainly
should consider it intelligent.
• The Turing test is a one-sided test.
– The observer could interact with the machine
and a human by teletype.
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– This test would satisfy most people but not all
philosophers.
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• The Turing Test
• Turing's 1950 article Computing Machinery
and Intelligence discussed conditions for
considering a machine to be intelligent.
– He also may have been the first to decide that
AI was best researched by programming
computers rather than by building machines.
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• Turing gave a lecture on AI in 1947.
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– After WWII, a number of people independently
started to work on intelligent machines.
– English mathematician Alan Turing may have
been the first.
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• Discussions of AI hold to two common
distinctions:– Weak and Strong AI
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• Weak AI holds that suitably programmed
machines can simulate human cognition.
• Strong AI, by contrast, maintains that
suitably programmed machines are capable
of cognitive mental states.
• The most well known attack on strong AI is
John Searle's Chinese Room thought
experiment.
• Searle's target is a computer program which
allegedly interprets stories the way humans
can by reading between the lines and
drawing inferences about events in the story
which we draw from our life experience.
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– Proponents of strong AI say that the program in
question
• (1) understands stories, and
• (2) explains human
ability to understand stories.
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• In response, Searle offers the following
thought experiment.
– Suppose that a non -Chinese speaking person is
put in a room and given three sets of Chinese
characters (a script, a story, and questions about
the story).
– Although the man does not know the meaning
of the Chinese symbols, he gets so good at
manipulating symbols that from the outside no
one can tell if he is Chinese or not Chinese.
• For Searle, this goes against both of the
above two claims of strong AI.
– He also receives a set of rules in English which
allow him to correlate the three sets of
characters with each other (i.e., a program).
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• Application of AI
• game playing
– You can buy machines that can play master
level chess.
• There is some AI in them, but they play well against
people mainly through brute force computation-looking at hundreds of thousands of positions.
• To beat a world champion by brute force and known
reliable heuristics requires being able to look at 200
million positions
per second.
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Damien Costello, Dept of Computing &
Maths, GMIT
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• speech recognition
• understanding natural language
• computer vision
– The world is composed of three-dimensional
objects, but the inputs to the human eye and
computers' TV cameras are two dimensional.
– Some useful programs can work solely in two
dimensions, but full computer vision requires
partial three-dimensional
information.
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• expert systems
• MYCIN
– A “knowledge engineer” interviews experts in a
certain domain and tries to embody their
knowledge in a computer program for carrying
out some task.
– One of the first expert systems was MYCIN in
1974, which diagnosed bacterial infections of
the blood and suggested treatments.
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– It did better than medical students or practicing
doctors, provided its limitations were observed.
– Namely, its ontology included bacteria,
symptoms, and treatments and did not include
patients, doctors, hospitals, death, recovery, and
events occurring in time.
– Its interactions depended on a single patient
being considered.
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• heuristic classification
– To put some information in one of a fixed set of
categories using several sources of information.
• Advising whether to accept a proposed credit card
purchase.
• Information is available about the owner of the
credit card, his record of payment and also about the
item he is buying and about the establishment from
which he is buying it.
• One of the most important things to emerge
from AI research is:– Intelligence requires knowledge
• Besides being indispensable knowledge has
other properties:– voluminous
– hard to characterise accurately
– constantly changing
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• To actually use AI to solve problems
– the system must contain a lot of knowledge if it
is to handle anything but trivial toys
– As the amount of knowledge grows, it becomes
harder to access the appropriate things when
needed, so more knowledge must be added.
• But now there is even more knowledge to manage,
so more must be added (vicious circle).
• The knowledge should be represented such
that:– It captures generalisations.
– It can be understood by people who must
provide it.
– It can be easily modified to correct errors and
reflect changes in the world and our changing
view or the world.
• What’s the point?
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Damien Costello, Dept of Computing &
Maths, GMIT
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– It can be used in many situations even if not
totally accurate and complete.
– It can be used to help overcome its own sheer
bulk by helping to narrow the range of
possibilities that must be considered.
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Damien Costello, Dept of Computing &
Maths, GMIT
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