Cellular Automata Theory and Experiment (MIT Press ed)

Cellular automata, dynamic systems in which space and time are discrete, are yielding applications in both the physical and natural sciences.

The 34 contributions in this book cover many aspects of studies on cellular automata and include reviews, research reports, and guides to literature and available software.

Chapters cover mathematical analysis, the structure of the space of cellular automata, learning rules as with specified properties: cellular automata in biology, physics, chemistry, and computation theory; and generalizations of cellular automata in neural nets, Boolean nets, and coupled map lattices.

Work on cellular automata may be viewed as revolving around two central and closely related problems: the forward problem and the inverse problem.

The forward problem concerns the description of properties of given cellular automata.

Properties considered include reversibility, invariants, criticality fractal dimension, and computational power.

The role of cellular automata in computation theory is seen as a venue for exploring parallel computers as theoretical and practical tools in mathematical physics. The inverse problem involves designing rules that possess specified properties or perform specified task.

Studies of the inverse problem take up the organization and structure of the set of automata, in particular the parameterization of the space of cellular automata.

Optimization and learning techniques, like the generic algorithm and adaptive stochastic cellular automata are applied to find cellular automaton rules that model such physical phenomena as crystal growth or perform such adaptive -learning tasks as balancing an inverted pole.