cellular automata

Since Life's inception, new similar cellular automata have been developed.

Class 3: Cellular automata which appear to remain in a random state.

There has been speculation that cellular automata may be able to model reality itself.

A number of papers have analyzed and compared these 256 cellular automata.

Often, models like cellular automata are used to help understanding of processes that work in real life.

Thus was born the first system of cellular automata.

Use of cellular automata is common but not required.

This is also a well-known issue in cellular automata.

Cellular automata provide a simple model to aid in the understanding of excitable media.

At York, he developed an in-depth study into musical composition using cellular automata.

The rule 30 and rule 110 cellular automata are particularly interesting.

Two dimensional cellular automata are used for random number generation.

Among other contributions, he was known for applying cellular automata in the social sciences.

Nevertheless, Rule 184 is fundamental to traffic simulation by cellular automata.

Golly is a tool for the simulation of cellular automata.

Self-organization has also been observed in mathematical systems such as cellular automata.

Certain types of cellular automata can be used to generate mazes.

Class 1: Cellular automata which rapidly converge to a uniform state.

A professor at the University of Chile, he is known for his work on cellular automata.

When these are approximated by cellular automata, they often yield similar patterns.

This property is useful in the applications of cellular automata to physical simulation.

There is a connection between quantum-dot cells and cellular automata.

In retrospect, White recognizes this first project as an early cellular automata experiment.

Unlike the cells in cellular automata, agents can be mobile with respect to space.

Such cellular automata have rules specially constructed to be reversible.