Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 5: Marginally below acceptance threshold
review_content: This paper is trying to use GAN to model chess. The authors claim to use the discriminator to regularize the generator, which is doing “style transfer”.

However, as chess is a problem which is solved by traditional search. In this paper I can’t find evidence that this generative model is better than search. I can guess the speed is better than search, but seems no evidence overall it is better.

For “style”, there is an explanation of style transfer is minimizing Maximum Mean Discrepancy [1]. In this paper, I can’t find any relation of the method is related to MMD style.

[1] https://arxiv.org/abs/1701.01036 

review_title: null

final_score: rating: 5: Marginally below acceptance threshold
review_content: This work aims to address the playing chess with a known style by merging two popular ideas GAN and style transfer.

I see two issues:
1) we do not see an evaluation on different players and overall performance, instead just one configuration. Although workshop format is almost like a proof-of-concept, a single configuration results should not be enough to convince reader that this is a viable solution/direction.

2) Evaluation of style is extremely hard even in this domain. Evaluating on target style move vs predicted move is trivial way to go. However, no chess player can predict what a certain person would play in isolation. In other words, the style in game playing is not about a single move, it is about a sequence of moves. A creative solution to this evaluation problem would definitely increase the quality of this work.
review_title: evaluation

Decision: Reject