zero__one
Joined 3 years ago
Karma
124
Submissions
13
Member since
Jan 9
Recent submissions
Comment · 28 days ago
Stickvania is a stick-figure demake of Konami's original Castlevania for the Nintendo Entertainment System. The game reduces the castle, enemies, objects, and Simon Belmont himself to simple line drawings that look like they were scribbled with a Sharpie.
Comment · 1 year ago
The bug is fixed. Though you'll need to refresh the app to get the latest.
Comment · 3 years ago
Here is a list of Surprising Turing-complete things:
https://www.gwern.net/Turing-complete
Wang tiles are on the list. Are they Turing-complete by themselves? Or do they become Turing-complete only when an agent attempts to arrange them?
Comment · 3 years ago
There are several issues with that comic:
1. In finite time, it is not possible for a human to create an infinite row of rocks.
2. The academic paper for Rule 110 describes it as "weakly universal" rather than Turing-complete because it depends on an infinite pattern.
3. The rocks act as RAM, not the CPU. The human is applying the rules. The human is the CPU.
In the Tetris paper, the agent is just a TAS recording while Tetris acts as RAM and the CPU.
Comment · 3 years ago
The same idea appeared earlier on this page:
Comment · 3 years ago
For the method described in the paper to work, the infinite version of the game requires a semihard drop operation or something analogous as a legal move. In modern versions of Tetris, the rotation systems permit very unusual operations such as wall climbing or infinite lock delays, not to mention rotating in physically impossible ways. A semihard drop is not a big ask on an infinite playfield. It's within the spirit of the game.
Comment · 3 years ago
From https://meatfighter.com/tetromino-computer/input-language.ht...
> In practice, IL programs direct the agent to construct a pile near the origin that progressively grows taller and wider. That being the case, it can work with a Tetris implementation that defines “row infinity” as a finite row whose index increases as a function of the number of spawns. In such an implementation, the agent emulates a semihard drop with a finite number of soft drops.