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Rule 30 is an elementary cellular automaton introduced by Stephen Wolfram in 1983. [2] Using Wolfram's classification scheme, Rule 30 is a Class III rule, displaying aperiodic, chaotic behaviour. This rule is of particular interest because it produces complex, seemingly random patterns from simple, well-defined rules.
In 2004, Matthew Cook published a proof that Rule 110 with a particular repeating background pattern is Turing complete, i.e., capable of universal computation, which Stephen Wolfram had conjectured in 1985. [2] Cook presented his proof at the Santa Fe Institute conference CA98 before publication of Wolfram's book A New Kind of Science.
WolframAlpha (/ ˈ w ʊ l f. r əm-/ WUULf-rəm-) is an answer engine developed by Wolfram Research. [3] It is offered as an online service that answers factual queries by computing answers from externally sourced data.
A second way to investigate the behavior of these automata is to examine its history starting with a random state. This behavior can be better understood in terms of Wolfram classes. Wolfram gives the following examples as typical rules of each class. [4] Class 1: Cellular automata which rapidly converge to a uniform state.
The Game of Life is undecidable, which means that given an initial pattern and a later pattern, no algorithm exists that can tell whether the later pattern is ever going to appear. Given that the Game of Life is Turing-complete, this is a corollary of the halting problem : the problem of determining whether a given program will finish running ...
The Wolfram Language (/ ˈ w ʊ l f r əm / WUUL-frəm) is a proprietary, [7] general-purpose, very high-level multi-paradigm programming language [8] developed by Wolfram Research. It emphasizes symbolic computation , functional programming , and rule-based programming [ 9 ] and can employ arbitrary structures and data. [ 9 ]
While earlier studies in cellular automata tended to try to identify types of patterns for specific rules, Wolfram's classification was the first attempt to classify the rules themselves. In order of complexity the classes are: Class 1: Nearly all initial patterns evolve quickly into a stable, homogeneous state.
The patterns generally have the form of either sequences or tree structures. Uses of pattern matching include outputting the locations (if any) of a pattern within a token sequence, to output some component of the matched pattern, and to substitute the matching pattern with some other token sequence (i.e., search and replace).