# SAI: a Sensible Artificial Intelligence that plays with handicap and targets high scores in 9x9 Go (extended version)

arXiv: https://arxiv.org/abs/1905.10863 · HTML: https://mlsanity.com/paper/1905.10863/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2019.

## Built on by (most cited first, 2 of 2)

- [Increasing biases can be more efficient than increasing weights](https://mlsanity.com/paper/2301.00924/) (2023)
- [Score vs. Winrate in Score-Based Games: which Reward for Reinforcement Learning?](https://mlsanity.com/paper/2201.13176/) (2022)

## Abstract (arXiv)

We develop a new model that can be applied to any perfect information two-player zero-sum game to target a high score, and thus a perfect play. We integrate this model into the Monte Carlo tree search-policy iteration learning pipeline introduced by Google DeepMind with AlphaGo. Training this model on 9x9 Go produces a superhuman Go player, thus proving that it is stable and robust. We show that this model can be used to effectively play with both positional and score handicap, and to minimize suboptimal moves. We develop a family of agents that can target high scores against any opponent, and recover from very severe disadvantage against weak opponents. To the best of our knowledge, these are the first effective achievements in this direction.