### N puzzle java

These java puzzles and their answers will help you in your next java coding interview. Learn their solution in advance, to make a better impression in your next interview. Do not forget to share more such puzzles if you have been asked — and you think they may help others. A farmer wants to reorganize the crops growing in his farm.

The farm is in the form of an N X M grid with each cell in the grid being a square plot. Each plot has to be planted with a variety of crop. The farmer has 26 varieties of crops that he can plant. The plant varieties are represented by lowercase English alphabets. He wants to follow the following condition while planting:In each row, there must be at least 2 different varieties of crops any number of crops can be used in a column No two nearby Top, bottom, left, right plots can have the same variety of crops.

Given the current state of the farm, find the minimum number of plots that have to replant with a different crop so that the above conditions are satisfied.

Input The first line contains two integers N and M, the dimensions of the farm. Output a single integer denoting the minimum number of plots that have to be replanted in order to satisfy the conditions imposed. Example 1 Input 4 4 acaa dddd bbbb ccce Output 6 Explanation: In this Example, We may replant the farm to look like: acac dede baba cece This arrangement of crop varieties satisfies the given conditions and the cost of this replacement is 6.Figure 1 shows an eight-puzzle.

The only valid moves are to move a tile which is immediately adjacent to the blank into the location of the blank. An example of such a move is to move tile 6 into the blank as is shown in Figure 2. Given any arrangement of the tiles, there are between two and four valid moves. The objective is to take a permutation of the tiles and the blank; and, by making a sequence of valid moves, to transform the puzzle into the original shown in Figure 1.

### Java Interview Puzzles with Answers

Figure 3 shows a permutation with a single move which places 6 into the correct location. Given a permutation, a solution is a sequence of moves which transforms the permutation into the solution.

Figure 3. A move in a permutation of the eight-puzzle. Without the hash table, objects in the heap could not be easily accessed and therefore the run time would be slowed significantly. For example, Black hashes to 4 and has the highest priority, therefore it is in the 1st location of the heap and the index 1 is stored in the node. Similarly, Orange hashes to 7 and has priority lower than Brown. Being stored in index location 4, the node in the hash table stores 4. There are three distances which can be used to measure the distance between the state of a puzzle and the solution:.

Figure 5. The solution to the eight-puzzle and a permutation of the tiles. The discrete distances between the permutation and the solution is 1 they are different.

Using the Hamming distance, the distance is 8—only one tile is in the correct location. This is shown on the left of Figure 6.

Figure 6. The Hamming and Manhattan distances of the permutation from Figure 5.

The class also tracks the size and the maximum size of the heap the maximum number of objects in the priority queue. Dijkstra's algorithm found the minimum solution of 24 moves after having considered possible solutions visited vertices during the search and the maximum size of the heap was Using the Hamming distance, the number of puzzles considered dropped to This small reduction is almost certainly due to the fact that the Hamming distance is only really useful in the last stages of finding the solution.

The maximum heap size was still N-Puzzle or sliding puzzle is a popular puzzle that consists of N tiles where N can be 8, 15, 24 and so on. The puzzle consists of one empty space where the tiles can be moved and thus the puzzle is solved when a particular goal pattern is formed like the following is one of the variant goal pattern.

So first of all we need to decide what all information is needed to be kept in the node. Now as we discussed earlier, we need to realize the search space as a graph. The following will be the expanded graph in the next epoch when the available moves are implemented. Each node can have maximum of 4 children, the graph fill further expand in a similar fashion with each child pointing to the parent using pointer.

Just as the name suggests, this heuristics returns the number of tiles that are not in their final position. This heuristic is however the slowest and a huge amount of nodes will be explored to reach the goal state compared to other heuristics.

In this instance we see that tile 4 and tile 1 are in a linear conflict since we see that tile 4 is in the path of the goal position of tile 1 in the same column or vice versa, also tile 8 and tile 7 are in a linear conflict as 8 stands in the path of the goal position of tile 7 in the same row. Hence here we see there are 2 linear conflicts. As we know that heuristic value is the value that gives a theoretical least value of the number of moves required to solve the problem we can see that one linear conflict causes two moves to be added to the final heuristic value h as one tile will have to move aside in order to make way for the tile that has the goal state behind the moved tile and then back resulting in 2 moves which retains the admissibility of the heuristic.

Linear Conflict combined with Manhattan distance is significantly way faster than the heuristics explained above and 4 x 4 puzzles can be solved using it in a decent amount of time. Just as the rest of the heuristics above we do not consider the blank tile when calculating linear conflicts.

Pattern databases are the fastest compared to the other heuristics. A fringe database is when you take a part of the N-Puzzle i. For example in this picture.

Static Additive pattern database is faster than fringe pattern database as it does not take the maximum cost among the groups but it adds the cost required of all the pattern groups since the cost to goal state of each pattern is calculated without considering the other group i.

Static additive pattern databases can be of various forms according to the memory or processing power available like the following shown for puzzle. It is obvious that Partitioning will take a lot of memory but will be the fastest. A dynamically partitioned additive pattern database considers conflicts between each groups and thus is the fastest.

In this way we consider all the permutations of positions of the tiles in the group including the blank tile but insert into the database without considering the blank tile, thus in this way we have a single permutation of a group with all the positions of blank tile while the group remains the same, but inserting into database the least cost of all the states with all positions of blank tile of a single permutation of the group. It explains in detail on making pattern database and all you need to know about creating pattern databases.

The basic algorithms changes but rest everything remains as it is. If used with proper heuristics it can solve Puzzle in a few seconds.

There can be things wrong with this as I am still a beginner on the path of learning and this is based on what I have learnt so far with examples from external sources as well.

So experts, please let me know through the comments section. Like Like. Got into right article after so much research with right explanation. Crisp and clear!!!

Developing a 15 Puzzle - Game of Fifteen in Java 8 with Eclipse

The pseudocode and the explanation are really helpful, however I am facing a snag.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I'm trying to implement a program to solve the n-puzzle problem.

I have written a simple implementation in Java that has a state of the problem characterized by a matrix representing the tiles. I am also able to auto-generate the graph of all the states giving the starting state. On the graph, then, I can do a BFS to find the path to the goal state. But the problem is that I run out of memory and I cannot even create the whole graph. I tried with a 2x2 tiles and it works. Also with some 3x3 it depends on the starting state and how many nodes are in the graph.

But in general this way is not suitable. So I tried generating the nodes at runtime, while searching.

## Puzzle Game in Java

It works, but it is slow sometimes after some minutes it still have not ended and I terminate the program. Btw: I give as starting state only solvable configurations and I don't create duplicated states. So, I cannot create the graph. I need the whole graph, right? Any suggestion? Basically here is what I do: - Solver 's solve has a SortedSet. I think it should work because: - I keep all the visited states so I'm not looping. But it does not work. Or at least, after a few minutes it still can't find a solution and I think is a lot of time in this case.

EDIT 2 Here are my heuristic functions:. If I use a simple greedy algorithm they both work using Manhattan distance is really quick around iterations to find a solutionwhile with number of misplaced tiles it takes around 10k iterations. Comparators are like that:. EDIT 3 There was a little error. Or at least, for the 3x3 if finds the optimal solution with only iterations. For the 4x4 it's still too slow.

I left it for 10 minutes and it didn't end. If BFS consumes much memory it is normal. But I don't know exactly fro what n it would make problem. Use DFS instead.This web application allows you to view a graphical representation of a range of different graph search algorithms, whilst solving your choice of 8-puzzle problems.

Maximus xi a2

On the left-hand side of this application, you will see the Control Panel. Using the Control Panelyou can configure the following aspects of the application:.

To set the Initial or Goal states, you can click either the 'Edit state' button or the graphical representation of the state. To change a tile, simply click on the tile that you would like to replace, then enter the new value on your keyboard. This will swap the tile with the one that previously held that value.

N-Puzzle supports five different Graph-based Search Algorithms. The first three are Uninformed Search Algorithms:.

N-Puzzle can be used in two modes. The default is Single-Step mode, which allows you to 'rewind' a search, one step at a time. This is useful for getting a better understanding of how a Search Algorithm works.

V episode 1 1983

The other mode is Burst Mode. Once started, Burst Mode continues running a search until the goal state has been found. A Burst Mode search can be paused, but cannot be 'rewound'.

While a search is active, you will be able to see a visual representation of the search tree. Each node in this search tree represents an arrangement of tiles or stateand is drawn as a box that is split into 4 sections. Below the puzzle state are two sections.

## Implementing A-star(A*) to solve N-Puzzle

The section on the left is the depth of the node. The section on the right is the heuristic score. The heuristic score is only used with Informed Search Algorithms, so if you are using Breadth-first, Depth-first or Iterative Deepening Search, the heuristic score will be omitted.

The last section records the order in which nodes were expanded. For example, the root node will always be ' 1', and the next node to expanded will be marked as ' 2'.

To help make the Search Tree more 'readable', the border of each node is colour-coded based on its current state. While the search algorithms are running, two lists are maintained.A python script that implements a generic planner to solve a series of minigames using heuristic algorithms to generate the best possible moves to reach the goal state.

Add a description, image, and links to the n-puzzle topic page so that developers can more easily learn about it. Curate this topic. To associate your repository with the n-puzzle topic, visit your repo's landing page and select "manage topics.

Dm verity disabler s7

Code Issues Pull requests. Updated Oct 19, C. Updated Nov 21, Python. Star 2. Updated Feb 6, C. A List of all the Artificial Intelligence Assignment. Updated May 8, Python. Star 1. Implementation of algorithms to solve N-Puzzles in haskell. Updated Jan 10, Haskell.

Star 0. A collection of game AI. Updated Oct 11, Python. Updated Mar 26, Python. AI Lab Offlines and Onlines. Updated Dec 2, Java. Updated Dec 2, Python. Updated Sep 29, Java.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. The dark mode beta is finally here.

Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I'm trying to implement a program to solve the n-puzzle problem.

Simplicity zt 16 44 for sale

I have written a simple implementation in Java that has a state of the problem characterized by a matrix representing the tiles. I am also able to auto-generate the graph of all the states giving the starting state. On the graph, then, I can do a BFS to find the path to the goal state. But the problem is that I run out of memory and I cannot even create the whole graph. I tried with a 2x2 tiles and it works.

Also with some 3x3 it depends on the starting state and how many nodes are in the graph. But in general this way is not suitable. So I tried generating the nodes at runtime, while searching. It works, but it is slow sometimes after some minutes it still have not ended and I terminate the program.

Btw: I give as starting state only solvable configurations and I don't create duplicated states. So, I cannot create the graph.

Captain chords crack reddit

I need the whole graph, right? Any suggestion? Basically here is what I do: - Solver 's solve has a SortedSet. I think it should work because: - I keep all the visited states so I'm not looping. But it does not work. Or at least, after a few minutes it still can't find a solution and I think is a lot of time in this case.

EDIT 2 Here are my heuristic functions:. If I use a simple greedy algorithm they both work using Manhattan distance is really quick around iterations to find a solutionwhile with number of misplaced tiles it takes around 10k iterations.

Comparators are like that:. EDIT 3 There was a little error. Or at least, for the 3x3 if finds the optimal solution with only iterations. For the 4x4 it's still too slow. I left it for 10 minutes and it didn't end.