Shogi AI built to play like people.

Why we built Mino AI

Being weaker is not the same as being human.

You can make a powerful engine easier. Cut its search short, or make it pick its fourth-best move now and then, and it will lose more often. But it does not start playing like a beginner. It plays like a very strong player having a seizure: twenty moves of flawless technique, then a piece left hanging for no reason, then flawless technique again. Nothing about that teaches you what a real opponent does, because no real opponent does it.

Human mistakes have shape. A beginner does not choose a random legal move; they push the wrong pawn, miss a lance coming down an open file, leave their king a little too exposed and not know it yet. Those are specific, and they are learnable, because millions of people have made them in games that were written down.

What it gives back

Mino does not return a move. It returns a probability across every legal move: its estimate of how likely a player at that strength is to choose each one. The opponent you face is a draw from that distribution, which is why two games never open the same way.

How it works

One model, many strengths of play.

Mino reads the position along with who is playing it: how strong the player is, how strong their opponent is, and how far into the game they are. It does not return one answer. It returns a probability across every legal move, which is its estimate of how likely a human of that strength would be to play each one.

Shogi Castle builds its opponents from that distribution. A higher level draws from a stronger player’s move choices and picks among them more decisively; a lower level draws from a weaker player’s and wanders more. Every move it plays is a move real people play in real games. The weakest opponent is not a random one, it is a plausible one.

A ladder you can climb

Because the strength of a Mino opponent comes from the move distribution rather than from a crippled search, the levels are smooth. Each one is measurably stronger than the one below it, and each one still plays recognisable shogi.

What it learned from

Mino was trained on millions of positions from publicly available human shogi games, across the full range of playing strengths, from beginners to strong amateurs.

Its training goal is deliberately simple. Show it a position, tell it how strong the player to move is, and ask it to predict the move that player actually chose. Nothing about “best” enters into it. Everything Mino knows about shogi, it learned from watching people play.

Tested against players it has never seen

A model that has memorised its training games will look excellent and teach you nothing. So Mino is measured on players whose games were removed from its training entirely, not merely on positions it did not see. Whole games are held back, and only the held-out player’s own moves are scored.

On that held-out test it does best exactly where it was designed to: predicting the moves of beginner and intermediate players, the part of the strength range where computer opponents have always felt least like people.

The right engine for the right job

Mino is not built for maximum playing strength and does not claim it. A model that predicts human moves cannot out-calculate a search engine, and it is not supposed to.

So Shogi Castle uses both. Mino plays the human-like opponents, where feeling natural matters more than being correct. Conventional engines handle expert-strength play and position analysis, where being correct is the entire point.

Mino was trained on publicly available game records from lishogi.org, an open-source shogi server. Shogi Castle is not affiliated with or endorsed by lishogi. No player names or account data are used in the model.