Whenever AI techniques solve a problem, Very soon thereafter, the technique is no longer considered an AI technique, but becomes more mainstream CS.
The two definitions try to avoid the whole issue of defining Artificial or defining Intelligence and will do for now.
At least they define the boundaries of the problem.
Early AI (symbolic):
One possible Classification:
Mundane problems, Formal problems, Expert Problems.
Simple reasoning and logical symbol manipulation
To solve these problems we must explore a large number of solutions quickly and choose the Best One.
Proving Properties of Programs
Manipulate Symbols and reduce problem (usually recursively), until the answer is obvious. That is, it can be looked up in a table.
Rule based systems
(if (conditions) then action)
The PHYSICAL SYMBOL SYSTEM HYPOTHESIS
A physical symbol system (such as a computer) has the necessary and sufficient means for general intelligent action
In other words:
Computers (Turing Machines) have the power for general intelligent action.
Solve real-world problems that are computationally infeasible using other techniques.AI tools for representing knowledge, reasoning and assembling systems.
Explaining kinds of intelligence. Which ideas about representation, algorithms, and implementations explain various kinds of intelligence.
Learn a set of tools for REPRESENTING and MANIPULATING knowledge.
Three dimensional noughts and crosses.
Learn basic approaches that deal with LEARNING or ADAPTATION
After this course:
You can confidently solve any problem that you could be confronted with. Or at least say that it is too hard with current technology. Point out areas of improvement (AI related) in any software you develop.You will be in a better position to handle hard problems and will be able to advise on whether AI will be useful for a particular project.
Software developers with an AI background are some of the best developers. They not only have to deal with hard problems but also limited resources. This is one area where computing power is never enough and you need to be creative in implementation.
LISP is often called the language of AI but this is not a programming course and so no LISP...
Use any language you like for the assignment.
AI and other disciplines:
SCIENCE and ENGINEERING:
Knowledge based systems: Areas of study for which you require expertise.
AI and psychology are related because psychology is the study of human behavior (intelligent behavior), so can we learn by copying? AI want to make COMPUTATIONAL equivalents of anything learned from study of human behavior. Unfortunately copying does not always work consider the aeroplane.
LINGUISTICS...similar to the above
AI and BIOLOGY are related for similar reasons. Neural Networks (NNs) came out of this kind of study of Brains (human and animal).
For example, studying the human visual system gives a good model for computational vision.
Genetic Algorithms (GAs) are algorithms that mimic evolution to learn, design, and adapt over time.
Characteristics of Intelligent systems:
One basic theme in AI
A representation is a set of conventions about how to describe a class of entities. A good representation often makes it very easy to solve a problem.
farmer, hungry fox, fat goose, delicious grain.
How to cross the river?
English language representation is hard.
Try visual/graphical representation:
F G M X ~
F X ~ M G
M F X ~ G
F ~ M G X or X ~ M F G
M F G ~ X M G X ~ F
G ~ M F X
M G ~ F X
~ M G F X
The representation is more visual
This is an example of a SEMANTIC NET
The REPRESENTATION PRINCIPLE
Once a problem is described using an appropriate representation, the problem is almost solved.
A Good Representation:
links define the possible paths from a given node;
A transition is possible if there is a link else NOT
A Representation has four PARTS
A SEMANTIC NET is a representation
Constructors construct nodes