Table 3 shows some of the privels and their attribute values for the first fortress problem simage.
| Table 3: Privels from Fortress Problem Simage 1 | |||||
| Visual Object | attributes | value | |||
| Fortress | looks-like: | generic-visual-element | |||
| location: | center | ||||
| Bottom-road | looks-like: | line | |||
| start-point: | bottom | ||||
| end-point: | fortress | ||||
| Right-road | looks-like: | line | |||
| start-point: | right | ||||
| end-point: | fortress | ||||
| Left-road | looks-like: | line | |||
| start-point: | left | ||||
| end-point: | fortress | ||||
| Top-road | looks-like: | line | |||
| start-point: | top | ||||
| end-point: | fortress | ||||
| Soldier-path | looks-like: | line | |||
| location: | bottom-road | ||||
| thickness: | thick | ||||
We represented the fortress story with three simages (see Figure 2.) The first was a representation of the original fortress problem. It had four roads, represented as thick lines, radiating out from the fortress, which was a generic-visual-element in the center. We represented the original soldier path as a thick line on the bottom road. This simage was connected to the second with a decompose privit, where the arguments were soldier-path1 for the object and ``four'' for the number-of-resultants. The second simage shows the soldier-path1 decomposed into four thin lines, all still on the bottom road. The lines are thinner to represent smaller groups. This is connected to the final simage with the move privit, which is applied to three of the new soldier paths. They are sent to the different roads. The final simage in the fortress problem shows all four soldier paths, each on a different road.
We represented the start state of the tumor problem as a single simage. The tumor itself is represented as a generic-visual-element. The ray of radiation is a thick line that passes through the bottom body part.
Galatea transfers the first transformation (decompose) from the source analog (the solved fortress problem) to the target (the tumor problem). It knows which part of the tumor problem to apply this privit to from the given analogical mapping between the first simages of the fortress and tumor problems. Galatea generates a second simage with the line representing the ray decomposed into four thinner lines. In the next iteration Galatea successfully transfers the second transformation, moving each of the rays to the different roads.
Galatea can solve analogical transfer problems using only visual knowledge, as we have shown with the fortress/radiation example. Though this work is still in progress, we conjecture that this theory, when Privlan is more fleshed out, will apply to all problems whose solution constraints involve visually perceivable states of the world. Another sense of this is: if you can make a diagram of it, our theory applies to it.