Week 4 – the main topic was AI within board games. The first session was a discussion about different types of board games and AI techniques it could use. The tasks were related to board games too – first to create a game tree for a game of choice. I have chosen a game of Checkers – this is a two-player game, which is zero-sum (one player wins, one player loses).
To win is to remove all of the enemy’s checkers – an interesting rule involved is that given the opportunity a player has to remove enemy’s piece. Even though it’s played on a chessboard, the number of moves involved is smaller than with chess, since pieces only move one space diagonally on black spaces – the first turn has only 8 possible moves. This means that the MiniMax algorithm could work nicely for this game. There could be better ones such as AB Pruning, but for simplicity’s sake MiniMax should suffice.
The second task was in relation to tactical games and use AI in them – we got asked to investigate the Condensation Algorithm, which outputs the best possible positions based on tactics used using a grid and separates locations that are not connected. It is actually called Conditional Density Propagation algorithm.
Next question was about non-military games using group coordination. I think it can be used in those, with any games where using a crowd of characters is used, example being games such as Pikmin. It could be used in coordination with boids to create an illusion of realistic birds being controlled.
With the Zombie game it’s the last stretch of the basics is inclusion of random spawning of enemies to make it more difficult and like a game. This means figuring how many zombies we want at any given time, and where to spawn them from.