Depth, balancing, and limits of the Elo model

Abstract

Much work has been devoted to the computational complexity of games. However, they are not necessarily relevant for estimating the complexity in human terms. Therefore, human-centered measures have been proposed, e.g. the depth. This paper discusses the depth of various games, extends it to a continuous measure. We provide new depth results and present tool (given-first-move, pie rule, size extension) for increasing it. We also use these measures for analyzing games and opening moves in Y, NoGo, Killall Go, and the effect of pie rules.

DOI: 10.1109/CIG.2015.7317964

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Cite this paper

@article{Cauwet2015DepthBA, title={Depth, balancing, and limits of the Elo model}, author={Marie-Liesse Cauwet and Olivier Teytaud and Hua-Min Liang and Shi-Jim Yen and Hung-Hsuan Lin and I-Chen Wu and Tristan Cazenave and Abdallah Saffidine}, journal={2015 IEEE Conference on Computational Intelligence and Games (CIG)}, year={2015}, pages={376-382} }