Lecture 3: Artificial Intelligence
23/09
Artificial intelligence (AI) is the ability of a digital
computer or computer-controlled robot to perform
tasks commonly associated with intelligent beings.
What is the aim?
Perhaps to mimic human intelligence and learn
something about our brains in the process. Or perhaps
it is just to achieve useful results.
Early AI focused on expert systems, which are
computer programmes that have at their disposal
‘expert’ knowledge in a certain area, and that can ‘use’
this knowledge to answer questions, solve cases, give
advice etc. There are two components:
1. Knowledge base: This consists of formalisation of
the available ‘knowledge’ in a certain domain in a
certain form, such as:
a. Production rules – Consist of an IF (condition)
and a THEN (conclusion). For example,
i. If it rains, wear your rainboots.
ii. If there is reasonable suspicion, then you
have a suspect.
It also includes CHAINING: the conclusion of
one production rule may be the condition of
another. For example,
i. If you wear your rainboots, don’t go
inside.
ii. If you have a suspect, police have certain
powers.
The use of Boolean operators (and, or and not)
can then make up more complicated
conditions and conclusions automated tools
to check for inconsistencies and gaps etc.
23/09
Artificial intelligence (AI) is the ability of a digital
computer or computer-controlled robot to perform
tasks commonly associated with intelligent beings.
What is the aim?
Perhaps to mimic human intelligence and learn
something about our brains in the process. Or perhaps
it is just to achieve useful results.
Early AI focused on expert systems, which are
computer programmes that have at their disposal
‘expert’ knowledge in a certain area, and that can ‘use’
this knowledge to answer questions, solve cases, give
advice etc. There are two components:
1. Knowledge base: This consists of formalisation of
the available ‘knowledge’ in a certain domain in a
certain form, such as:
a. Production rules – Consist of an IF (condition)
and a THEN (conclusion). For example,
i. If it rains, wear your rainboots.
ii. If there is reasonable suspicion, then you
have a suspect.
It also includes CHAINING: the conclusion of
one production rule may be the condition of
another. For example,
i. If you wear your rainboots, don’t go
inside.
ii. If you have a suspect, police have certain
powers.
The use of Boolean operators (and, or and not)
can then make up more complicated
conditions and conclusions automated tools
to check for inconsistencies and gaps etc.
