Non-Discrete Automata
Posted: May 31st, 2023, 8:00 pm
After studying differential calculus, a thought came to my mind: Could CA be simulated through a system of equations? The answer is no, but it got me thinking about non-discrete systems. The following are 2 potential alternative systems to discrete automata.
1. State-continuous CA:
Generally, there are a finite number of states any given CA. However, this isn't necessary. For a totalistic rule, a cell would compute the sum of the states of each of it's neighbors, and according to it's birth and death rules, compute each. Then, it would interpolate (using an interpolation curve) between the two values according to it's state. In addition to an infinite range of states, the birth and death rules can be computed for an infinite number of inputs. In the non-totalistic rulespace, a specialized higher dimensional function would define the weights of each birth rule, essentially assigning a strength to each rule. I have no clue how to implement such a higher dimensional function, but one would need to exist in order for non-totalistic rules.
2. Position-continuous CA:
In the case of a CA without cells, or just an "A", The grid would be replaced by a scalar field, and each point in the scalar field would use a predetermined convolutional mask as a replacement for a sum.
I do not see this being useful as a means of actual prediction, as by definition, this would require infinite accuracy. They would be unpredictable, inconsistent, and getting repeatable results from such systems would be impossible. However, it is an interesting idea.
Time continuous CA would be possible to implement, but incredibly computationally difficult.
1. State-continuous CA:
Generally, there are a finite number of states any given CA. However, this isn't necessary. For a totalistic rule, a cell would compute the sum of the states of each of it's neighbors, and according to it's birth and death rules, compute each. Then, it would interpolate (using an interpolation curve) between the two values according to it's state. In addition to an infinite range of states, the birth and death rules can be computed for an infinite number of inputs. In the non-totalistic rulespace, a specialized higher dimensional function would define the weights of each birth rule, essentially assigning a strength to each rule. I have no clue how to implement such a higher dimensional function, but one would need to exist in order for non-totalistic rules.
2. Position-continuous CA:
In the case of a CA without cells, or just an "A", The grid would be replaced by a scalar field, and each point in the scalar field would use a predetermined convolutional mask as a replacement for a sum.
I do not see this being useful as a means of actual prediction, as by definition, this would require infinite accuracy. They would be unpredictable, inconsistent, and getting repeatable results from such systems would be impossible. However, it is an interesting idea.
Time continuous CA would be possible to implement, but incredibly computationally difficult.