*Semantic networks* are a knowledge representation scheme.

This lesson will cover the following topics:

- Knowlege representations
- Semantic networks
- Problem-solving with semantic networks
- Represent & Reason

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.

- Label all objects (x is a circle, y is the diamond, z is the black dot), and reference them as nodes
- Represent the relationships between nodes, in both states (frames), both A and B.
- Represent the transformation between the nodes between states, A and B.

- 1. Lexically: nodes
- 2. Structurally: directional links
- 3. Semantically: application-specific labels

- Make relationships explicit
- exposese natural contraints
- bring objects and relations together
- exclude extraneous details
- transparent, concise, complete, fast, computable

- Three guard and three prisoners must cross river.
- Boat may take only one or two people at a time.
- Prisoners may never outnumber guards on either time (thought prisoner may be alone on either coast).

**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:

- 5: Unchanged
- 4: Reflected
- 3: Rotated
- 2: Scaled
- 1: Deleted
- 0: Shape Changed

Lesson preview:

- Generate and test method
- Smart Generators
- Smart Testers
- Examples

Takes one state, and generates all possible states, however, should not generate states that are not productive.

Analyzes all generated states, and filters to only legal (i.e., those states that adhere to logical constraints) and productive (i.e., resultant states that have not yet been observed) states.

**Note**: For each problem, we have to find the right balance the smartness of generators and testers.

- Setup semantic network for A and B, and construct transformation.
- Apply transformation to C to generate D
- Compare D to possible solutions and find highest match (via level of confidence).
- Ensure that D meets a confidence level threshold.
- If not, repeat 1-4, however using new semantic network construction approach.

- Setup semantic network for A and B and constuct transformation