Symbolic Knowledge Representation

• A related topic and one that covers the rest of the learning outcomes is representing the symbols.
• There are lots of ways of representing symbols and these align with types in programming languages.
• So, a WM item or a CBR feature might be an enumerated type, an integer, a boolean or a real.
• It might also be an object.
• We can also use more sophisticated techniques like Logic, Semantic Nets, and Frames.
• Logic is about boolean values, and simple connectives.
• It also takes advantage of the (well used) concept of functions. For this reason I could have put logic on the prior page.
• Semantic Nets are about nodes representing concepts and arcs representing relations between concepts.
• I could use a semantic net in a RBS by using an isa operation.
• For example If (X is red) And (X isa bird) Then (X is cardinal)
• Similarly, I could use a semantic distance measurement (number of arcs crossed from one node to another) as part of the similarity metric in a CBR system
• Symbols are relatively easy for computers to work with, but they have some problems. They don't accurately represent the world. So is (Tall Chris) a true fact? That really depends.
• You should know when symbols are appropriate and when they are not.