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Billda

Member Since 18 Apr 2013
Offline Last Active Apr 29 2013 06:06 AM

Posts I've Made

In Topic: Bomberman AI approach

19 April 2013 - 10:22 AM

Next approach i was planning to try was with game tree algorithms - minimax, alpha-beta pruning... At this point i realized that every article i found about minimax assumed that players alternate with moves. In bomberman this isnt true, everybody make action at the same time. With that information. If i have 4 players and 5 actions (moves and bomb place), at every cycle there could be 5^4 possibilities of actions. It is quite a large number and in game tree with depth 3 it is impossible to search.



So my question is - is there any better way? Or any algorithms that are based on this type of game where everyone make move at the same time? Or game tree is in this situation bad idea?

Not only does minimax require alternating turns, but it also only works for two players. There is another paradigm for building AI for board games that is way more flexible: Monte Carlo Tree Search. It can handle multiple players, simultaneous decisions and randomness without much trouble.

The best go programs use MCTS, even though it is in the class of games where minimax can be applied. The main reason for this is that, unlike minimax, MCTS doesn't require an evaluation function, and nobody knows how to write a decent evaluation function for go.

http://senseis.xmp.net/?MonteCarloTreeSearch
http://senseis.xmp.net/?UCT (UCT is a specific algorithm of the MCTS family)

If anyone has better links for MCTS, please post them.

Thank you very much, it seems like exactly what i need


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