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  2. Parsons problem - Wikipedia

    en.wikipedia.org/wiki/Parsons_problem

    Parsons' programming puzzles became known as Parsons puzzles [2] and then Parsons problems. [3] Parsons problems have become popular as they are easier to grade than written code while capturing the students problem solving ability shown in a code creation process.

  3. Answer set programming - Wikipedia

    en.wikipedia.org/wiki/Answer_set_programming

    An early example of answer set programming was the planning method proposed in 1997 by Dimopoulos, Nebel and Köhler. [3] [4] Their approach is based on the relationship between plans and stable models. [5] In 1998 Soininen and Niemelä [6] applied what is now known as answer set programming to the problem of product configuration. [4]

  4. Problem solving environment - Wikipedia

    en.wikipedia.org/wiki/Problem_solving_environment

    The Problem Solving Environment for Parallel Scientific Computation was introduced in 1960, where this was the first Organised Collections with minor standardisation. [2] In 1970, PSE was initially researched for providing high-class programming language rather than Fortran, [ citation needed ] also Libraries Plotting Packages advent.

  5. How to Solve It - Wikipedia

    en.wikipedia.org/wiki/How_to_Solve_It

    Russian inventor Genrich Altshuller developed an elaborate set of methods for problem solving known as TRIZ, which in many aspects reproduces or parallels Pólya's work. How to Solve it by Computer is a computer science book by R. G. Dromey . [ 29 ]

  6. Stochastic dynamic programming - Wikipedia

    en.wikipedia.org/wiki/Stochastic_dynamic_programming

    Originally introduced by Richard E. Bellman in (Bellman 1957), stochastic dynamic programming is a technique for modelling and solving problems of decision making under uncertainty. Closely related to stochastic programming and dynamic programming , stochastic dynamic programming represents the problem under scrutiny in the form of a Bellman ...

  7. Nonlinear programming - Wikipedia

    en.wikipedia.org/wiki/Nonlinear_programming

    an infeasible problem is one for which no set of values for the choice variables satisfies all the constraints. That is, the constraints are mutually contradictory, and no solution exists; the feasible set is the empty set. unbounded problem is a feasible problem for which the objective function can be made to be better than any given finite ...

  8. Constraint programming - Wikipedia

    en.wikipedia.org/wiki/Constraint_programming

    Constraint programming (CP) [1] is a paradigm for solving combinatorial problems that draws on a wide range of techniques from artificial intelligence, computer science, and operations research. In constraint programming, users declaratively state the constraints on the feasible solutions for a set of decision variables.

  9. General Problem Solver - Wikipedia

    en.wikipedia.org/wiki/General_Problem_Solver

    General Problem Solver (GPS) is a computer program created in 1957 by Herbert A. Simon, J. C. Shaw, and Allen Newell (RAND Corporation) intended to work as a universal problem solver machine. In contrast to the former Logic Theorist project, the GPS works with means–ends analysis .