Semantic networks are a knowledge representation scheme.
This lesson will cover the following topics:
In each knowledge representation, there is a language, and that language has a vocabulary. In addition, the representation contains some content (or knowledge).
$$ F = ma $$
Force is equal to mass times acceleration
How to represent Raven’s Progressive Matrices using a semantic network.
State A, and state B.
Lexicon: Consider each node to be a unique state, represented by:
- number of prisoners and guards on left side
- number of prisoners and guards on right side
- side that boat is on.
Which transitions (e.g. moves) between states are both legal AND productive?
Represent total possible states given transformations possible:
|trans:||init:<||2p to R:>||p to L:<||2p to R:>||p to L:<||2g to R:>||g.p to L:<||2g to R:>||p to L:<||2p to R:>||p to L:<||2p to R:<|
|\(g_i\)||3, 0||3, 0||3, 0||3, 0||3, 0||1, 2||2, 1||0, 3||0, 3||0, 3||0, 3||0, 3|
|\(p_i\)||3, 0||1, 2||2, 1||0, 3||1, 2||1, 2||2, 1||2, 1||3, 0||1, 2||2, 0||0, 3|
One can weigh transformations to favor specific types of transformations over others. For example: