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.
Two distinct reader paths · historical publication dates reflect the research epoch, not when this collection was assembled.
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.
The original autoresearch loop found useful hypotheses—and then taught us why capped snapshots cannot crown a champion.
The day OverGrid stopped confusing character copy with behavioral identity.
Why OverGrid rejected two apparently sensible anti-loop rules before trusting another AI result.
An identical-agent control exposed a giant seat split in OverGrid’s old arena—and taught us to stop treating a leaderboard as an answer.
OverGrid archived a large AI dataset after two fairness confounds made its rankings untrustworthy.
The rules OverGrid adopted after a plausible AI lead vanished under more seeds, replies, and horizon.
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.
Why OverGrid introduced OGN beside verified replays—and what each format is designed to make possible.
How OverGrid playtests made deterministic AI research more useful.
An honest account of a non-promotion, an adaptive search breakthrough, and the difference between activation and strength.
Why game AI commentary needs the shared match situation—not hidden information, and not just the bot’s own score delta.
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 player’s guide to building asymmetric OverGrid threats that survive the opponent’s best cut.
How to protect an OverGrid lead using only the public counter-swings visible before the next hand is dealt.
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.
How to tell whether OverGrid pressure will convert into points—or disappear after one good reply.
A multiplayer OverGrid guide to tempo, last replies, and converting neutral territory into a durable lead.