Abstract
There are two classes of problem in the study of Cellular Automata. The forward. problem is the problem of determining the properties of the system. Solutions often consist of finding quantities that are computable on a rules table and characterizing the behavior of the rule upon repeated iterations, starting from different initial conditions. Solutions to the backwards problem begin with the properties of a system and find a rule or a set of rules which have these properties. This is especially useful in the application of Cellular Automata to the natural sciences, when researchers deal with a large collection of phenomena (Gutowitz, 1989). Another approach is to identify the basic structures of a Cellular Automaton (Adamatzky, 1995). Once these are known it becomes possible to develop specific models for particular systems and to detect general principles applicable to a wide variety of systems (Wolfram, 1984; Lam, 1998). According to Adamatzky, the identification of a system consists of two related steps, namely specification and estimation. In specification we choose a useful and efficient description of the system: perhaps an equation and a set of parameters. The second step involves the estimation of parameter values for the equation: exploiting measures of similarity.
TopBackground
One of the most spectacular characteristics of Cellular Automata is the emergence of complex structures, revealing their self-organizing capabilities. This kind of structure may emerge in a previously stationary domain against a range of different backgrounds. It can take many different forms. Some are stable; others decay immediately after they have come into being; others break down into substructures that reassemble after a certain lapse of time; others may generate new structures or absorb existing ones. When they collide, they may survive, they may be annihilated, or they may give rise to a cascade of new structures of increasing levels of complexity. The literature refers to some of these structures as “gliders”.