Edited by author 05-07-2001 03:35 AM
First, I agree with Bianca’s analysis that some of the experiments should have been justified a bit better. Knowing how good the algorithms that their system was played against would be extremely helpful.

One thing that I found interesting that the paper began to bring up was what effect does the player that the system trains against have on the system. The paper mentions that the main influence that different opponents has is to present different states to the system. I would be curious what the training would be like with a totally random player. An analysis of the effects on training with different types of players may lead to incites into a better learning algorithm. Maybe reinforcement learning is not the best approach for this domain.

Also, along the same lines as Melanie's message, I think that the authors need to be careful to explicitly state the goals of the paper. If the goal of the paper is to create the best Othello player, then some of their choices may be questionable (such as simply searching 2 ply ahead). A finely tuned min-max algorithm that employs various pruning tactics may be able to due an extremely good job. When combined with heuristics, this may make for a powerful competitor (as has been the case with games such as Chess). However, if the goal of the paper was an exploration of reinforcement learning in this domain, then the paper may be more appropriate. Either way, the paper should make the goals and ideas investigated a bit clearer.

Dave