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Details of the Intelligent System.

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Details of the Intelligent System

What is a System?

Intelligent systems


A system is part of the universe, with a limited extension in space and time. What is outside the frontier of the system, we call its environment. Stronger or more correlations exist between one part of the system and another than between this part of the system and parts in the environment.


What is an Intelligent System?


An intelligent system learns how to act so it can reach its objectives. It is a system that learns during its existence. (In other words, it learns, for each situation, which response permits it to
reach its objectives.) It continually acts, mentally and externally, and by acting reaches its objectives more often than pure chance would indicate. For acting, and for its internal processes, it consumes energy.
Note: For this definition, examples of an intelligent system would be humans, animals, and “artificial” intelligent systems, such as artificial insects and mobile autonomous systems, and humanoid robots.

Details of the Intelligent System
Details of the Intelligent System


Details of the Intelligent System

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The main processes occurring within the intelligent systems are the following:


i. The Intelligent System has a temporary objective, that it has derived from its main objective.
ii. It senses its environment, although we have to realize that it has only a few senses and that these can only capture, for instance, light and sound of an object, but can not capture or know the object itself.
iii. The system then stores these sense impressions as elementary concepts. Concepts are a material way of storing information. Working on concepts creates new ones and stores relationships to other total, part, abstract and concrete concepts. Of course, there is a difference between an object or occurrence in the environment, the concept the system uses for its internal processing, and the word it uses to communicate the concept. To continue with the internal processes, in more intelligent systems there should now be a check of the incoming information.
iv. With all the information, expressed as concepts, the system builds up the present situation. Now it looks into its memory and finds applicable response rules.
v. It chooses one of the best it has found and performs the corresponding action. Response rules are a field of storage that includes the present situation to which the rule is applicable and the corresponding action.
vi. The intelligent system continually records the present situation and the action that followed as a response rule. The very first response rules are due to chance actions and to teaching.
vii. When the system is externally inactive, that is it sleeps, it reviews the response rules stored in its memory and performs some generalizations.

Intelligent systems
Intelligent systems

It makes abstractions of concepts and creates the corresponding response rules, including these abstractions. Further comparisons are between the situation and action of a series or recently learned response rules as well as comparisons between situations of different response rules and between actions of different response rules. By all these activities, starting with very concrete response rules, it create response rules that are applicable to several different but similar situations. After some while, its memory is full and it forgets the least used concepts and response rules.
Now you have gained detailed knowledge of Intelligent Systems. You have observed the role played by concepts, be they concrete, composite, or abstract and the role played by response rules. You have seen that most observed activity in Intelligent Systems is based on response rules, chosen for the present sub-objective.


Intelligence

Intelligence based on both process and outcome.
o The process should be significant in some sense.
o The outcome should be reasonable with expectations.
 Process and outcome can be further qualified.
o Process can be open-ended, capable of handling unexpected input.
o Outcome can be optimal.
Intelligence as Computation
 The outcome is a result of the computation.
 Intelligence is computation organized to produce particular outcomes.
o We know computations.
 This is a controversial point of view (deep AI).
o But it replaces a vexing issue with a comfortable one.

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A Computation

what color is(x)
switch x
case “snow” : return “white”
case “grass”: return “green”
case “sky” : return “blue”
default : return “don’t know”
what color is(“snow”) → “white”
what color is (“mud”) → “don’t know”


Another Computation


what color is(x)
if is color(x, y) return y
what color is(x)
if is made of(x, y) and is color(y, z)
return z
what color is(x)
return “don’t know”
is color(“snow”, “white”)
is made of(“grass”, “vegetation”)
is color(“vegetation”, “green”)
what color is(“snow”) → “white”
what color is(“grass”) → “green”
what color is (“mud”) → “don’t know”

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Intelligent Computations


 How do these two computations differ?
o Which one is the more “intelligent” computation?
o They’re both the same with respect to outcomes.
 Is the strange second computation more intelligent than the first computation?
o Hint: why is grass green?


Intelligent Computation


 The second computation has a particular structure:
o It has a set of facts arranged as a knowledge base.
o is color(“snow”, “white”)
o It has a general (and strange) computational action (reasoning rules).
o what color is(x)
o if is made of(x, y) and is color(y, z)
o return z


Structural Intelligence

 An intelligent computation has a particular structure:
o A collection of facts (the data structure)
 And a representation for those facts.
o A set of reasoning rules for dealing with facts (the algorithm).
 This intelligent structure then produces intelligent outcomes.

Details of the Intelligent System
Details of the Intelligent System
Computer Misuse Act 1990


Remarks


 Isn’t this just all a bunch of smoke and mirrors?
o Can’t you write regularly structured programs to do the same thing?
 You can, but
o Writing the regular program is hard without understanding the intelligent program.
o Providing equivalent generality is hard and expensive.
o Machine translation is the classic example.


Science v Engineering


 The difference between science and engineering captures the difference nicely.
o Artificial intelligence is science.
o Intelligent systems is engineering.
o Regular programs are technology.
 This is an engineering course.
o The translation to technology is an optimization.


Summary

 Intelligence is a particular computational structure.
o The structure produces intelligent outcomes.
 The structure relies on special data structures, knowledge bases, and algorithms, reasoning rules.
o Varying data structures and algorithms produce varying structures.

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