Knowledge   Definition: Is the organized set of data and information that solve a particular problem or make a decision.  Types:     

A priori. A posteriori. For procedure. Declarative. Tacitus.

Knowledge 

Knowledge Acquisition   Definition: A process of getting together the knowledge that the system will need before the conceptualization.  It is a process that never ends.  Participants:    

Expert. User. Knowledge Engineer. Sources (Static and Dynamic).

Acquisition of a dynamic source



 This is done after the acquisition of static knowledge.  Techniques:    

Manuals. Semiautomatic. Automatic. Used by a group of experts.

Manual techniques   Interviews:  Types:  Initial.  Unstructured.  Structured.

 Questionnaires.  Extraction mediating tight corners.  Protocol analysis.  Observation of the expert.

Semiautomatic techniques

  Psychological Scaling: Technique used to produce structured representations of knowledge from the expert analysis.

 Checkers: Technique where the expert must give values ​to the characteristics of the domain elements that want to be evaluated.

Automatic techniques   Algorithms used:  Winston.  Mitchell's version space.  Decision Trees.

Used by a group of experts

  Brainstorming: Consists that the team of experts express the views it has on the problem.  Nominal Group Technique: Ideas are generated in writing, then argue and discuss and finally proceed to a vote of the solutions.  Delphi Method: Consists that the experts completed anonymously and phased questionnaires will feed back the views of others.

Knowledge Representation

  There are different methods:  Predicate logic (deduction, abduction, induction).  Frames or marks.  Objects.

 Associative Networks (Graphs, Neural Networks).