Version 1.0, August 31, 2001, Copyright, Hugh Jack 1993-2001

16.1 OVERVIEW

 

· What is Intelligence ? - Nobody knows, but there are lots of ideas.

 

· So if we don't know what intelligence is, how can we write programs that are intelligent ? - AI researchers guess at how parts of the brain work, then try to reproduce the effect in a program.

 

· A Myth is that artificial programs use intelligence to guide themselves, but they are not as stupid as traditional programs.

 

· Most AI methods take ONE aspect of human thinking, and then model it, such as,

  1. - Logical Reasoning
  2. - Recognition
  3. - Association

 

· There are many older, and newer AI topics of interest,

  1. - Expert Systems
  2. - Planning Systems
  3. - Search Techniques
  4. - Fuzzy Logic
  5. - Neural Networks
  6. - Genetic Algorithms
  7. - Symbol Manipulation
  8. - etc.

 

· Each of the AI techniques is distinct in which applications they are useful for, and how they are applied.

 

· In manufacturing there are many problems which are difficult to solve using computers, such as trying to get a computer to `listen for a particular PING when doing inspection'. But, AI can help solve some of these problems.

 

· AI is not magic, you must still understand the problem before an AI system will help you solve it.

 

· The List below says a few words about some popular AI topics, and how they relate to applications,

 

  1. Expert Systems - These systems use exact rules with true or false conditions, and results. "If the engine stops, the car is out of gas"
  2.  
  3. Fuzzy Logic - This method still uses rules which do not need true or false conditions. "When I want the engine to go slower, I give it less gas" where slower, and less are somewhat arbitrary values.
  4.  
  5. Neural Networks - This methods is equivalent to learning by example. "I don't know why, I just know to go faster, I push the accelerator that much".

 

· For the three methods above, how well the problem can be described determines which is too be used. In effect, if the problem is very logical, use expert systems, if you can still make rules, but nothing is true or false use fuzzy logic. Finally, if you can't define rules for solving a problem, but can do it by intuition, use neural networks.