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.
Structure:
Semantic:
Which transitions (e.g. moves) between states are both legal AND productive?
Represent total possible states given transformations possible:
i | 0 | 1 | 2 | 3 | 4 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
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: