I have got an assignment in my AI class to design a good time management plan for a player in the game of Blokus.
the player is given a fixed amount of time for the all game and he needs to distribute it wisely over his moves.
the player uses the alpha-beta search algorithm in an iterative deepening framework to find the best move.
im given an implementation of the game where the player statically distributes his time evenly over the max amount of moves (21) and i need to improve upon that.
my question is how should i dynamically distribute the time between moves using the iterative deepening framework to my advantage?
after a lot of searching this is the closest thing i could find about time management (in chess) but it is not explained:
Iterative deepening in conjunction with its predictable effective branching factor allows a flexible time management either to terminate the current iteration and to fall back on best move and PV of the previous iteration, or to decide about termination prior to start a new iteration or to search a next root-move.
how do you calculate that predictable effective branching factor and why is it important for the decision of the ID termination?
any help would be greatly appreciated!