gpt4

I'm doing Prolog experiments.

The grid is 1-indexed.

Top-Left Bottom-Right (TLBR) is used for object bounding boxes, like tY_lX_bY_rX where t=top l=left b=bottom r=right.

The width of the object bounding box has a 'w' prefix, like 'w5' is width=5. The height of the object bounding box has a 'h' prefix, like 'h3' is height=3.

The number of solid pixels in the object has a 'm' prefix, like 'm12' is mass=12.

The id prefixed text has no integer value and should not be considered for sorting. The number of unique IDs may be relevant. The total mass of certain IDs is sometimes preserved in the output.

% Example 1 input grid_width3_height3
object(input1_idPj8kdf4_t1_l1_b3_r3_w3_h3_m5_shapePlus_scalex1_scaley1, transform(all)).
object(input1_idPfe7a8k_t3_l1_b3_r1_w1_h1_m1_shapeRectangle_scalex1_scaley1, transform(all)).
object(input1_idP3d53ef_t1_l3_b1_r3_w1_h1_m1_shapeRectangle_scalex1_scaley1, transform(all)).

% Example 1 output grid_width6_height6
object(output1_idPj8kdf4_t1_l1_b6_r6_w6_h6_m20_shapePlus_scalex2_scaley2, transform(all)).
object(output1_idPfe7a8k_t5_l1_b6_r2_w2_h2_m4_shapeRectangle_scalex2_scaley2, transform(all)).
object(output1_idP3d53ef_t1_l5_b2_r6_w2_h2_m4_shapeRectangle_scalex2_scaley2, transform(all)).

% Example 2 input grid_width2_height2
object(input2_idP33ffe7_t2_l1_b2_r1_w1_h1_m1_shapeRectangle_scalex1_scaley1, transform(all)).
object(input2_idPfe7a8k_t1_l1_b1_r1_w1_h1_m1_shapeRectangle_scalex1_scaley1, transform(all)).
object(input2_idP3d53ef_t1_l2_b2_r2_w1_h2_m2_shapeRectangle_scalex1_scaley2, transform(all)).

% Example 2 output grid_width4_height4
object(output2_idP33ffe7_t3_l1_b4_r2_w2_h2_m4_shapeRectangle_scalex2_scaley2, transform(all)).
object(output2_idPfe7a8k_t1_l1_b2_r2_w2_h2_m4_shapeRectangle_scalex2_scaley2, transform(all)).
object(output2_idP3d53ef_t1_l3_b4_r4_w2_h4_m8_shapeRectangle_scalex2_scaley4, transform(all)).

% Example 3 input grid_width4_height4
object(input3_idPj8kdf4_t4_l4_b4_r4_w1_h1_m1_shapeRectangle_scalex1_scaley1, transform(all)).
object(input3_idP33ffe7_t1_l3_b4_r4_w2_h4_m5_shapeUpTack_scaleUnknown, transform(rot270_flip90)).
object(input3_idPfe7a8k_t1_l1_b2_r1_w1_h2_m2_shapeRectangle_scalex1_scaley2, transform(all)).
object(input3_idP3d53ef_t2_l2_b2_r2_w1_h1_m1_shapeRectangle_scalex1_scaley1, transform(all)).

% Example 3 output grid_width8_height8
object(output3_idPj8kdf4_t7_l7_b8_r8_w2_h2_m4_shapeRectangle_scalex2_scaley2, transform(all)).
object(output3_idP33ffe7_t1_l5_b8_r8_w4_h8_m20_shapeUpTack_scaleUnknown, transform(rot270_flip90)).
object(output3_idPfe7a8k_t1_l1_b4_r2_w2_h4_m8_shapeRectangle_scalex2_scaley4, transform(all)).
object(output3_idP3d53ef_t3_l3_b4_r4_w2_h2_m4_shapeRectangle_scalex2_scaley2, transform(all)).

There number of output objects can be different than the input objects. Also consider the rules with clockwise rotation. Check if a condition is satisfied only for objects with a certain shape. A shape can occlude another shape, so shapeL may appear as shapeRectangle. Sometimes it's the occluded object that gets transformed. Consider both euclidian distance and manhatten distance between objects. Check how much an object moves relative x, y. Check how IDs gets swapped. Don't invent new IDs. Objects that stay stationary may be a useful landmark. A landmark may be a starting point for inserting a new object next to. Check that the examples have landmarks.

Check if objects are aligned to a certain edge. Check if the mass is preserved. Count the number of object groups. Check if all the output objects agree on the same shape. Check if the shape may be a shortest path drawn between two landmarks and navigates around obstacle objects. For the objects that make it to the output, check if their shape is preserved. A line that goes from edge to edge, some condition may be satisfied above the line, but not below it. Consider where two objects are touching, maybe the touch points are different in the output, such as moving from left side to the opposite side. Consider the touch point may be at the center of another object, so objects can be moved to the center point. Check how many times an object is duplicated, does it correspond to the mass of another object or some other property. Transformations: sort, gravity towards, rotate, flipx, flipy, move, copy, merge objects, split objects, extract object, fit object inside another object, layout 2..5 objects in a direction while keeping the objects aligned and evenly spaced (remember to update tlbr coordinates). The transformation can be anything, it just have to be simple.

Task A

Think step by step, what does the examples output objects have in common. Check if they are all horizontal lines. Are they sorted in a particular way.

Task B

Think step by step, what are the transformations across all the examples, that goes from the input to the output. Write down your observations. Explain your reasoning for inserting new objects. Explain your reasoning for deleting existing objects.

Task C

Think step by step about the orientation of the output objects. Does it make sense to layout the objects in another direction. Update the object coordinates accordingly.

Task D

With the following example, I want you to predict what the output should be. Print your reasoning before the prolog code.

% Example 4 input grid_width5_height5
object(input4_idPz7ea0g_t1_l5_b1_r5_w1_h1_m1_shapeRectangle_scalex1_scaley1, transform(all)).
object(input4_idP847fa3_t1_l4_b1_r4_w1_h1_m1_shapeRectangle_scalex1_scaley1, transform(all)).
object(input4_idP48kmo7_t3_l3_b4_r4_w2_h2_m3_shapeL_scalex1_scaley1, transform(rot270_flip180)).
object(input4_idPj8kdf4_t3_l2_b5_r2_w1_h3_m3_shapeRectangle_scalex1_scaley3, transform(all)).
object(input4_idP33ffe7_t4_l1_b4_r1_w1_h1_m1_shapeRectangle_scalex1_scaley1, transform(all)).
object(input4_idPfe7a8k_t1_l1_b2_r1_w1_h2_m2_shapeRectangle_scalex1_scaley2, transform(all)).
object(input4_idP3d53ef_t2_l2_b2_r3_w2_h1_m2_shapeRectangle_scalex2_scaley1, transform(all)).

% Example 4 output grid_width10_height10
PREDICT the text that starts with object(output4_

Repeat the previous example prolog code, with PREDICT replaced with your predictions.

Task A

After observing the input and output examples, there are a few key details that seem consistent:

Every object doubles its dimensions in width and height in the output (e.g., w1_h1 becomes w2_h2, w2_h2 becomes w4_h4, etc.).
The mass is increased according to the increase in dimensions.
The position of the objects changes; they tend to be pushed towards the top left corner, but not always.
Object's IDs are preserved in the output.
There are not necessarily the same number of objects in the output as in the input. In example 3, there are 4 objects in the input and 4 in the output, but they don't directly correspond to each other.

Task B

The transformations observed are:

Scaling: Each object is scaled by a factor of 2 in both the x (width) and y (height) directions, which is also reflected in the mass.
Positional change: Some objects move positions. They tend to move towards the top-left corner, but some objects move more horizontally, and some more vertically. It is possible that there is a pattern related to the object's shape or ID, but more data would be needed to confirm.
Inserting and deleting objects: In example 3, an additional object (idPj8kdf4) appears in the output that was not in the input. The reason could be related to the presence of the shapeUpTack object which has a transformation (rot270_flip90). It's possible the UpTack is acting as a guide for where to insert a new object.
Shape and transformation preservation: The shape and transformation of each object seem to be preserved, except for occluded objects which might appear as a different shape.

Task C

The orientation of the objects in the output is not significantly different from the input, but there is a shift towards the top left. However, the objects are not laid out in another direction. The transformation applied does not involve rotation, it maintains the same orientation but scales the objects.

Task D

Given the input, we would expect the output to continue the patterns observed earlier:

All objects double their size in both dimensions, and their mass increases accordingly.
Objects move, mostly towards the top-left.
The object with the idP3d53ef could be moved more horizontally towards the right, and the object with the idPfe7a8k more vertically towards the top, considering the observed behavior of these IDs in the examples.

% Example 4 output grid_width10_height10
object(output4_idPz7ea0g_t1_l9_b2_r10_w2_h2_m4_shapeRectangle_scalex2_scaley2, transform(all)).
object(output4_idP847fa3_t1_l7_b2_r8_w2_h2_m4_shapeRectangle_scalex2_scaley2, transform(all)).
object(output4_idP48kmo7_t5_l5_b8_r7_w4_h4_m12_shapeL_scalex2_scaley2, transform(rot270_flip180)).
object(output4_idPj8kdf4_t5_l3_b10_r4_w2_h6_m12_shapeRectangle_scalex2_scaley6, transform(all)).
object(output4_idP33ffe7_t7_l1_b8_r2_w2_h2_m4_shapeRectangle_scalex2_scaley2, transform(all)).
object(output4_idPfe7a8k_t1_l1_b4_r2_w2_h4_m8_shapeRectangle_scalex2_scaley4, transform(all)).
object(output4_idP3d53ef_t3_l3_b4_r6_w4_h2_m8_shapeRectangle_scalex4_scaley2, transform(all)).

The reasoning behind these predictions:

idPz7ea0g and idP847fa3 are moved towards the right, similar to the behavior of idP3d53ef in previous examples.
idP48kmo7 is centered.
idPj8kdf4 moves down like in previous examples.
idP33ffe7 moves towards the top, similar to the behavior of idPfe7a8k in previous examples.
idPfe7a8k is at the top left corner and doesn't move.
idP3d53ef moves horizontally to the right.