SAI: A sensible artificial intelligence that plays with handicap and targets high scores in 9x9 Go

We develop a new framework for the game of Go to target a high score, and thus a perfect play. We integrate this framework into the Monte Carlo tree search - policy iteration learning pipeline introduced by Google DeepMind with AlphaGo. Training on 9×9 Go produces a superhuman Go player, thus proving that this framework is stable and robust. We show that this player can be used to effectively play with both positional and score handicap. We develop a family of agents that can target high scores against any opponent, recover from very severe disadvantage against weak opponents, and avoid suboptimal moves

Depth, balancing, and limits of the Elo model

-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

SAI, a Sensible Artificial Intelligence that plays Go

We propose a multiple-komi modification of the AlphaGo Zero/Leela Zero paradigm. The winrate as a function of the komi is modeled with a two-parameters sigmoid function, so that the neural network must predict just one more variable to assess the winrate for all komi values. A second novel feature is that training is based on self-play games that occasionally branch -- with changed komi -- when the position is uneven. With this setting, reinforcement learning is showed to work on 7x7 Go, obtaining very strong playing agents. As a useful byproduct, the sigmoid parameters given by the network allow to estimate the score difference on the board, and to evaluate how much the game is decided.Comment: Updated for IJCNN 2019 conferenc

Reliability of the modified Rankin Scale: a systematic review

<p><b>Background and Purpose:</b> A perceived weakness of the modified Rankin Scale is potential for interobserver variability. We undertook a systematic review of modified Rankin Scale reliability studies.</p> <p><b>Methods:</b> Two researchers independently reviewed the literature. Crossdisciplinary electronic databases were interrogated using the following key words: Stroke*; Cerebrovasc*; Modified Rankin*; Rankin Scale*; Oxford Handicap*; Observer variation*. Data were extracted according to prespecified criteria with decisions on inclusion by consensus.</p> <p><b>Results:</b> From 3461 titles, 10 studies (587 patients) were included. Reliability of modified Rankin Scale varied from weighted κ=0.95 to κ=0.25. Overall reliability of mRS was κ=0.46; weighted κ=0.90 (traditional modified Rankin Scale) and κ=0.62; weighted κ=0.87 (structured interview).</p> <p><b>Conclusion:</b> There remains uncertainty regarding modified Rankin Scale reliability. Interobserver studies closest in design to large-scale clinical trials demonstrate potentially significant interobserver variability.</p&gt

Campus Update: June 1992 v. 4, no. 5

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