Start here: what OverGrid asks an AI to see.
A short guide to the game, its scoring, and why one three-piece turn can create a serious search problem.
OverGrid / AI Field Notes
Choose a behind-the-scenes account of the game AI, or a player-facing strategy story drawn from the same concrete problems.
10 notes for Search.
For game makers: engineering, evaluation, tooling, and failed experiments.
A short guide to the game, its scoring, and why one three-piece turn can create a serious search problem.
Why OverGrid’s first AI problem was not “which move is best?” but “which legal moves deserve a fair look?”
The first selective-reply experiments taught OverGrid not to turn every tactical question into a minimax sermon.
Why readable game records and public tactical receipts became research infrastructure.
Petra’s suspected missed height capture revealed why a good evaluator cannot rescue a move that never enters its menu.
Petra’s missed height tile exposed the real bounded-search problem: evaluating a move is useless if the candidate generator never offers it.
An honest account of a non-promotion, an adaptive search breakthrough, and the difference between activation and strength.
For players: transferable OverGrid decisions, routes, pressure, and counterplay.
A player’s guide to choosing between reply-backed calculation and a small, visible board proof.
A practical way to escape the first attractive OverGrid construction and find routes hidden by orientation.
A short OverGrid calculation routine for choosing between two constructions that both look good.