4 ms·

I think the metaphor is great. Each tile has a superposition of possible states that collapse into one observed state. That’s all the metaphor is meant to mean

by b33j0r 1y ago

I think the metaphor is great. Each tile has a superposition of possible states that collapse into one observed state. That’s all the metaphor is meant to mean, I think. What are better names? - Lego Simplices - Tile Constraint Pairing - Pipe Fitting - Cartesian Convolution (nah) - Finite automata (ok that’s fair, but subthings need names) I dunno, I think the WFC metaphor works for me. The “wavefunction” is just the finite set of states that have a non-zero probability of being observed.

kookamamie 1y ago
> Each tile has a superposition of possible states This is like saying an uninitialized integer has a superposition of all possible values. I find it a very convoluted way of saying "each tile has a set of possible next states" - dragging quantum terms to this is just confusing, in my opinion.
- b33j0r 1y ago
  You’re not wrong. I think I initially had higher expectations myself. But as a person who names things, I don’t really find this one to be a huge stretch. > This is like saying an uninitialized integer has a superposition of all possible values. Well? Yeah! And I personally like that way of thinking about sets. It maps pretty directly to my understandings of other things in math and physics.
  - kookamamie 1y ago
    Here's the algorithm described without the quantum nonsense: 1. Analyze Rules: Extract valid patterns (modules) and their compatibility rules (adjacency constraints) from input or define them. 2. Initialize Grid: Create an output grid where each cell initially contains all possible modules (maximum uncertainty). 3. Choose and Assign: Select the cell with the fewest valid modules remaining. Randomly assign one compatible module to it. 4. Propagate Constraints: Update neighboring cells by removing modules incompatible with the newly assigned one. If a cell loses all options, a contradiction occurs. 5. Handle Contradiction: If a contradiction arises, either backtrack to a previous choice or restart the process. 6. Repeat: Continue from step 3 until all cells are assigned a module or an unresolvable contradiction occurs.
    - rcxdude 1y ago
      Which is essentially how a basic sudoku solver works (which usually only has one solution, as opposed to many)
    - furyofantares 1y ago
      That's a very long name.
- ca_tech 1y ago
  The first definition of this type of procedural generation algorithm was called Model Synthesis by Paul Merrell [1] which built upon texture synthesis. You can even read Merrell's later comparison of the two algorithms [2]. [1] https://paulmerrell.org//thesis.pdf https://paulmerrell.org//thesis.pdf [2] https://paulmerrell.org/wp-content/uploads/2021/07/comparison.pdf https://paulmerrell.org/wp-content/uploads/2021/07/compariso...
Ygg2 1y ago
- Stohastic Sudoku solvers
gavinray 1y ago
> Tile Constraint Pairing This seems pretty solid to me.
IsTom 1y ago
Ad hoc Prolog
furyofantares 1y ago
Constraint Collapse would be good

by b33j0r 1y ago

kookamamie 1y ago

> Each tile has a superposition of possible states This is like saying an uninitialized integer has a superposition of all possible values. I find it a very convoluted way of saying "each tile has a set of possible next states" - dragging quantum terms to this is just confusing, in my opinion.

b33j0r 1y ago
You’re not wrong. I think I initially had higher expectations myself. But as a person who names things, I don’t really find this one to be a huge stretch. > This is like saying an uninitialized integer has a superposition of all possible values. Well? Yeah! And I personally like that way of thinking about sets. It maps pretty directly to my understandings of other things in math and physics.
- kookamamie 1y ago
  Here's the algorithm described without the quantum nonsense: 1. Analyze Rules: Extract valid patterns (modules) and their compatibility rules (adjacency constraints) from input or define them. 2. Initialize Grid: Create an output grid where each cell initially contains all possible modules (maximum uncertainty). 3. Choose and Assign: Select the cell with the fewest valid modules remaining. Randomly assign one compatible module to it. 4. Propagate Constraints: Update neighboring cells by removing modules incompatible with the newly assigned one. If a cell loses all options, a contradiction occurs. 5. Handle Contradiction: If a contradiction arises, either backtrack to a previous choice or restart the process. 6. Repeat: Continue from step 3 until all cells are assigned a module or an unresolvable contradiction occurs.
  - rcxdude 1y ago
    Which is essentially how a basic sudoku solver works (which usually only has one solution, as opposed to many)
  - furyofantares 1y ago
    That's a very long name.
ca_tech 1y ago
The first definition of this type of procedural generation algorithm was called Model Synthesis by Paul Merrell [1] which built upon texture synthesis. You can even read Merrell's later comparison of the two algorithms [2]. [1] https://paulmerrell.org//thesis.pdf https://paulmerrell.org//thesis.pdf [2] https://paulmerrell.org/wp-content/uploads/2021/07/comparison.pdf https://paulmerrell.org/wp-content/uploads/2021/07/compariso...

Ygg2 1y ago

- Stohastic Sudoku solvers

gavinray 1y ago

> Tile Constraint Pairing This seems pretty solid to me.

IsTom 1y ago

Ad hoc Prolog

furyofantares 1y ago

Constraint Collapse would be good