Mine Hunter

Mine Hunter pits the AI against a minefield! A known quantity of mines has been distributed across a grid of squares and the AI agent must work out where they are. To help, each uncovered square will show how many mines are in the surrounding squares.

The grid is represented as an array of characters:

Character	Description
F	A flagged mine
f	An incorrectly flagged empty space (game over)
#	A convered square
X	An exploded mine (game over)
.	An uncovered square with no neighbouring mines
1-8	An uncovered square with n neighbouring mines

and organised in a two dimentional array (with 0,0 at the top left).

Example board

JSON Representation

"board": [
    "#1",
    "#1",
    "11"
  ]

XML Representaion

<board>#1</board>
<board>#1</board>
<board>11</board>

Note: in this example the correct response would be to place a flag at 0,1.

Goal

Write an AI agent that can solve as many boards as possible, this can be measured by a moving average which is shown on the output.

Algorithms and Hints

There are quite a few algorithms that can be used to solve this scenario:

• Rules based solutions – These rely on recognising predefined (hand coded) patterns that occur in most games examples of these can be found on the minesweeper wiki.
• Constraints based solutions – A board can be considered as a series of constraint based problems, with each square being a mine (value 1) or not (value 0). Each numbered square is a constraint which needs to be met for a solution to be valid.
• Monti Carlo simulations – Rather than trying to deduce where the mines are, you may like to try an generate lots of random boards based on the number of remaining undiscovered mines. Filtering these solutions if they don’t match the available knowledge. Then estimating the probability of each uncovered square being a mine.
• Guessing – If all else fails, don’t be afraid to just guess!

API Interface

The Swagger specification for this API can be downloaded here.

In each step, the Sandbox Client will send the following information to the AI server:

• (Optional) the result of the last step, this consists of:
  • The boardID (unique identifyer) of the board being played.
  • The result at the end of the last move, either “PLAYING”, “WON”, “LOST”. If the result is PLAYING that means the previous moves were successful, but there are still uncovered or unflaged squares remaining. The new board will be a continuation of the last game. If the result is “WON” or “LOST” then a new board will have been generated.
• The boardID of the current board.
• The board represented as a series of character strings (see example above).
• The number of flags still to be placed.

The AI agent should respond with one or more moves, each move consists of:

• The location (x,y) of the move, with (0,0) being the top left square.
• Should this move place a flag (flag=true) or uncover a square (flag=false).

Note: there is no limit to the number of moves that can be included in each response. However if a move results in the game ending, subsequent guesses will be ignored rather than carried over to the next game.

JSON Example Request

{
  "lastMove": {
    "boardID": "1233-5678-90abc-2435",
    "result": "LOST"
  },
  "boardID": "1234-1234-1234-1234",
  "board": [
    "##1..",
    "##1..",
    "221..",
    "#1..."
  ],
  "flagsRemaining": 3
}

JSON Example Response

{
  "moves": [
    {
      "x": 1,
      "y": 1,
      "flag": true
    }
  ]
}

XML Example Request

<?xml version="1.0" encoding="UTF-8"?>
<MineRequest>
   <lastMove>
      <boardID>1233-5678-90abc</boardID>
      <result>LOST</result>
   </lastMove>
   <boardID>1234-1234-1234-1234</boardID>
   <board>##1..</board>
   <board>##1..</board>
   <board>221..</board>
   <board>#1...</board>
   <flagsRemaining>3</flagsRemaining>
</MineRequest>

XML Example Response

<?xml version="1.0" encoding="UTF-8"?>
<MineResponse>
    <moves>
        <x>1</x>
        <y>1</y>
        <flag>true</flag>
    </moves>
</MineResponse>