# Play adders and ladders online dating

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Snakes and Ladders: Multiplayer Board Game Online, You can compete against the computer or a friend while you play this online version of the classic board. Snakes and Ladders is an ancient Indian board game regarded today as a worldwide classic. It is played between two or more players on a gameboard having numbered, .. to generate statistics; Jain version of Snakes and Ladders explained in an interactive demonstration hosted by the Victoria and Albert Museum. sheptonmallet.info: Buy Sterling Classic 2-in-1 Ludo and Snakes & Ladders Board Game online at low price in India on sheptonmallet.info Contents: 1 x Game board, 1 x dice, 16 x coloured pawns; Can be played by . Date First Available, 15 March

There has even been evidence of a possible Buddhist version of the game existing in India during the Pala-Sena time period.

### Play Snakes and Ladders Dice Game Online

In Tamil Nadu the game is called Parama padam and is often played by devotees of Hindu god Vishnu during the Vaikuntha Ekadashi festival in order to stay awake during the night. In the original game the squares of virtue are: Faith 12Reliability 51Generosity 57Knowledge 76and Asceticism The squares of vice or evil are: The illustrations show good deeds and their rewards; bad deeds and their consequences.

Each player starts with a token on the starting square usually the "1" grid square in the bottom left corner, or simply, off the board next to the "1" grid square. Players take turns rolling a single die to move their token by the number of squares indicated by the die roll.

Tokens follow a fixed route marked on the gameboard which usually follows a boustrophedon ox-plow track from the bottom to the top of the playing area, passing once through every square. If, on completion of a move, a player's token lands on the lower-numbered end of a "ladder", the player moves the token up to the ladder's higher-numbered square. If the player lands on the higher-numbered square of a "snake" or chutethe token must be moved down to the snake's lower-numbered square.

If a player rolls a 6, the player may, after moving, immediately take another turn; otherwise play passes to the next player in turn. The player who is first to bring their token to the last square of the track is the winner. Variations[ edit ] Variants exists where a player must roll the exact number to reach the final square.

Depending on the variation, if the die roll is too large, the token either remains in place or goes off the final square and back again.

## Analysis of Chutes and Ladders

For example, if a player requiring a 3 to win rolls a 5, the token moves forward three spaces, then back two spaces. In certain circumstances such as a player rolling a 6 when a 1 is required to wina player can end up further away from the final square after their move, than before it.

In the book Winning Ways the authors propose a variant which they call Adders-and-Ladders and which, unlike the original game, involves skill. Instead of tokens for each player, there is a store of indistinguishable tokens shared by all players. The illustration has five tokens and a five by five board. There is no die to roll; instead, the player chooses any token and moves it one to four spaces. Whoever moves the last token to the Home space i. The theme of the board design is playground equipment, showing children climbing ladders and descending chutes.

The artwork on the board teaches morality lessons: Black children were depicted in the Milton Bradley game for the first time in Several Canadian specific versions have been produced over the years, including a version substituting Toboggan runs for the snakes.

We don't care how they got there, we just know that they roll the die again and act based on the results of the roll. If a player is at grid square G when he rolls again, one of six things could happen with equal probabilityand based on these probabilities the player would advance to one of the next squares. These probabilities can be represented as a sparse matrix which records the probability of moving from GridSpacei to GridSpacej by the entry in row-i and column-j.

We'll call this the Transition Matrix. A vanilla snippet of this matrix can be seen below. Now there is a chance that a roll will land a player onto the business-end of one of these special entities and they will get 'teleported' to a new location.

An example of the what the transition matrix would look like in this ficticious location is shown below. An example of this can be seen on row A roll of 3 will take the player to square 53, but a roll of 6 will also land the player on square 53 because landing on square 56 is the head of a snake which slides the player back to This is shown in the matrix snippet below.

The second condition to care about is the boundary scenario when the player is close to the finish. Here, according to our house rules, as an exact roll is not needed, there are multiple ways to get to square And watch out for the snake on square 98 which slides you back to square 78! Tokens start off-board, then the first roll lands the player on the board.

Well the simple explanation is that it is impossible for a player to rest on the head of a snake or the bottom of a ladder. You can see this in row98 above. Interestingly removing these redundant rows makes a non-trivial difference to calculation speed.

Matrix multiplication which as we will see below is used for this calculation is O n3 so reducing the size of a square matrix from to 82 doubles the speed! The transition matrix encapsulates the probability of moving from any square to any other square.

Now, all we need to do is provide it with an input. A player starts the game off board, and nowhere else, so we create a column vector with 1.

Next we multiply our column vector by the transition matrix, and the vector produced at the output is the probability distribution at the end of roll1. Each row value in the output vector is the probability that the player token will be in that square at the end of that roll.

On the grid, non-zero probabilities are painted in red.

The stronger the probability, the more intense the colour. After one iteration multiplicationthe results are pretty palpable. There are six shaded squares, each with equal probability representing the squares that would have been achieved with each distinct roll of the die. You can see that two of the rolls resulted in the use of ladders. We can use the output of the first roll as the input for the second roll. The output of the first roll shows the probability density of the grid. If we multiply probability density by the transition matrix again our output will be the probability density after two rolls A superposition of all the probabilities from rolling the die again from every position on the grid.

The dark shading of some of the cells especially on the lower rowhighlight the superposition of probabilities and show how these spaces are more likely to be occupied after two rolls because of the multiple ways to get there. Repeating this again, we get the state after three rolls. Here we can see that or maybe not, it's pretty faint! Seven rolls is the least number of rolls required to complete the game, and it is the first time that our probability cloud reaches this square.

Continuing on, here is a picture of the board after 10 rolls. You can see the colour in square getting deeper, as more and more games reach completion and the probability that a game would be completed increases. Here we are after 20 rolls. The pure-white squares show the start cells of the ladders and snakes.

**How to play Snakes and Ladders**

It is impossible zero probability for player to be on one of these squares. The very light cells are the ones with almost no chance of the player residing in them. Results To calculate the probability of completing a game after n-turns, we can examine the value in row of the output matrix after the initial identity input column vector has been multiplied by the TransitionMatrix n.

Here is a graph of the results: Actually, it's very close indeed. The only difference is that, not surprisingly, the Monte-Carlo is slightly less smooth. The curves are so close that when I plot them both the same graph, the lines obscure each other. To differentiate them, I've made the Markov generated line a little thicker and show a zoomed in portion of the curve in the image below.

The fact that the curves are so similar despite being generated in two very different ways cooroborates that our code is working correctly.