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  2. Rule 30 - Wikipedia

    en.wikipedia.org/wiki/Rule_30

    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.

  3. Linear multistep method - Wikipedia

    en.wikipedia.org/wiki/Linear_multistep_method

    The first Dahlquist barrier states that a zero-stable and linear q-step multistep method cannot attain an order of convergence greater than q + 1 if q is odd and greater than q + 2 if q is even. If the method is also explicit, then it cannot attain an order greater than q (Hairer, Nørsett & Wanner 1993, Thm III.3.5).

  4. Pattern matching - Wikipedia

    en.wikipedia.org/wiki/Pattern_matching

    In computer science, pattern matching is the act of checking a given sequence of tokens for the presence of the constituents of some pattern. In contrast to pattern recognition, the match usually has to be exact: "either it will or will not be a match." The patterns generally have the form of either sequences or tree structures.

  5. Rule 90 - Wikipedia

    en.wikipedia.org/wiki/Rule_90

    In Rule 90, each cell's value is computed as the exclusive or of the two neighboring values in the previous time step. Rule 90 is an elementary cellular automaton.That means that it consists of a one-dimensional array of cells, each of which holds a single binary value, either 0 or 1.

  6. WolframAlpha - Wikipedia

    en.wikipedia.org/wiki/WolframAlpha

    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.

  7. Q-learning - Wikipedia

    en.wikipedia.org/wiki/Q-learning

    Distributional Q-learning is a variant of Q-learning which seeks to model the distribution of returns rather than the expected return of each action. It has been observed to facilitate estimate by deep neural networks and can enable alternative control methods, such as risk-sensitive control.

  8. Rule 184 - Wikipedia

    en.wikipedia.org/wiki/Rule_184

    A state of the Rule 184 automaton consists of a one-dimensional array of cells, each containing a binary value (0 or 1). In each step of its evolution, the Rule 184 automaton applies the following rule to each of the cells in the array, simultaneously for all cells, to determine the new state of the cell: [3]

  9. A New Kind of Science - Wikipedia

    en.wikipedia.org/wiki/A_New_Kind_of_Science

    The basic subject of Wolfram's "new kind of science" is the study of simple abstract rules—essentially, elementary computer programs.In almost any class of a computational system, one very quickly finds instances of great complexity among its simplest cases (after a time series of multiple iterative loops, applying the same simple set of rules on itself, similar to a self-reinforcing cycle ...

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