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Named after the number of tiles in the frame, the 15 puzzle may also be called a "16 puzzle", alluding to its total tile capacity. Similar names are used for different sized variants of the 15 puzzle, such as the 8 puzzle, which has 8 tiles in a 3×3 frame. The n puzzle is a classical problem for modeling algorithms involving heuristics.
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.
[15] [16] If the code employs a strong reasoning algorithm, incorporating backtracking is only needed for the most difficult Sudokus. An algorithm combining a constraint-model-based algorithm with backtracking would have the advantage of fast solving time – of the order of a few milliseconds [17] – and the ability to solve all sudokus. [5]
The minimum number of flips required to sort any stack of n pancakes has been shown to lie between 15 / 14 n and 18 / 11 n (approximately 1.07n and 1.64n), but the exact value is not known. [2] The simplest pancake sorting algorithm performs at most 2n − 3 flips.
Some of the puzzles are well known classics, some are variations of known puzzles making them more algorithmic, and some are new. [4] They include: Puzzles involving chessboards, including the eight queens puzzle, knight's tours, and the mutilated chessboard problem [1] [3] [4] Balance puzzles [3] River crossing puzzles [3] [4] The Tower of ...
Examples of problems that can be modeled as a constraint satisfaction problem include: Type inference [3] [4] Eight queens puzzle; Map coloring problem; Maximum cut problem [5] Sudoku, crosswords, futoshiki, Kakuro (Cross Sums), Numbrix/Hidato, Zebra Puzzle, and many other logic puzzles
In a typical backtracking solution to this problem, one could define a partial candidate as a list of integers c = (c[1], c[2], …, c[k]), for any k between 0 and n, that are to be assigned to the first k variables x[1], x[2], …, x[k]. The root candidate would then be the empty list (). The first and next procedures would then be
The search algorithm uses the admissible heuristic to find an estimated optimal path to the goal state from the current node. For example, in A* search the evaluation function (where n {\displaystyle n} is the current node) is: