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Member Since 16 Apr 2013
Offline Last Active Apr 24 2013 06:31 PM

#5054315 Heuristic for minimax - Board Game 'Odd'

Posted by on 17 April 2013 - 03:26 PM

Right now I am using  iterative deepening AB minimax with memory(MTD(f)) + Monte Carlo search as the evaluation function and it is performing better than my earlier minimax algorithm and better than a basic Monte Carlo search.


The algorithm is performing like a Monte Carlo search in the beginning: the search depth is in the range [1-4] (branching factor ~ 122 and the leaf nodes are evaluated using Monte Carlo search which takes time)  but since it's storing the node's evaluation in a transposition table, this guides minimax later when the search can go deep towards the end of the game. I am trying to find a good combination of Monte Carlo and minmax especially that the current implementation suffers from the horizon effect and has a very small search depth at the beginning.


I am thinking about the following approaches to improve my player:

- Improve the evaluation function (Alvaro's suggestions sound reasonable) and maybe use quiescence search to mitigate the horizon effect.

- Implement my AI using a purely Monte Carlo search (it's Aheuristic nature might work for this game)

#5053926 Heuristic for minimax - Board Game 'Odd'

Posted by on 16 April 2013 - 12:24 PM

I am implementing an AI player for this board game.

I am using AB minimax with memory (MTD(f)) for search, but I am having trouble finding a good heuristic for the evaluation function, and even if I want to use reinforcement learning I need to find a good set of features to represent the board states since the state space is very large. How should I approach this problem?